Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery

Baoru Huang

REVIEW 4 major objections 4 minor 199 references

Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This thesis shows a simple regression network can predict a laparoscopic gamma probe's sensing spot on tissue directly from standard RGB images and probe-axis points.

desk verdict Novel problem formulation and a genuinely useful dataset, but the benchmark claim rests on a train/test split that likely lets the network interpolate between phantom rotations of the same pose rather than generalize to new poses. read the letter →

arxiv 2501.01752 v1 pith:GBGOAQSO submitted 2025-01-03 eess.IV cs.CVphysics.med-ph

Baoru Huang This is my paper · ORCID

classification eess.IVcs.CVphysics.med-ph

keywords gammaprobesensingareadetectionlaparoscopicsurgerysurgicalvisionregressionnetworkradio-guidedaxis-surfaceintersectionintraoperativevisualisation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis argues that a surgeon using a tethered laparoscopic gamma probe during minimally invasive cancer surgery can be shown, in real time, exactly which spot on the tissue surface the probe is sensing. The central move is to stop treating the task as a 3D geometry problem — reconstructing the tissue surface and the probe pose — and instead treat it as a 2D inference problem: predict the probe-axis/tissue intersection point directly from an ordinary laparoscopic RGB image plus a few points sampled along the probe's apparent axis. To get training labels, the author replaces the probe's sensing tip with a laser-module mock probe, so the visible laser spot marks the ground-truth intersection; after training, the laser is no longer needed. The best configuration, a ResNet image branch combined with a simple MLP for the axis points, runs at 50 frames per second and achieves the reported benchmark on two newly released datasets. If this holds in the operating theatre, it would give surgeons a direct visual cue of where gamma signals originate, potentially reducing incomplete resections and enabling augmented-reality guidance.

What carries the argument

The load-bearing object is the laser-module mock probe: a non-functional gamma-probe shell fitted with a red laser whose visible spot on the phantom marks the probe axis-tissue intersection, providing the ground truth label. The load-bearing method is a two-branch regression network: a ResNet50 branch encodes the laser-off stereo RGB image, an MLP branch encodes 50 points sampled along the probe axis extracted by PCA, the two feature vectors are concatenated, and an MSE loss regresses the 2D intersection point. Paired laser-on/laser-off image acquisition, an electrically controlled shutter, and a rotation stage generate the Jerry and Coffbee datasets, with structured-light depth maps in Coffbee supplying 3D error evaluation. The identity that carries the argument is that the laser spot centre equals the sensing-area ground truth, so training on laser-off images transfers to the real probe situation where no spot exists.

What would settle it

Mount a real gamma probe with a fixed laser module so both axes are aligned, place a small radioactive source under a phantom, scan the probe across the surface, and record where the gamma count peaks; compare that peak location with the network's predicted intersection point and with the laser spot. If the predicted point is consistently off by more than the reported 3D error while the laser spot agrees with the gamma peak, the method's labels are wrong; if the laser spot itself disagrees with the gamma peak, the mock-probe assumption collapses.

Watch

Extended reading notes

Core claim

The paper's central claim is that the sensing area of a laparoscopic gamma probe — defined as the intersection of the probe axis with the tissue surface, projected into the 2D laparoscopic image — can be predicted directly from the standard 2D image and the probe's apparent axis, without per-pixel depth, without tracking markers, and without the laser module at inference time. The problem is reformulated as laser-point inference: a DAQ-controlled laser module mounted in a non-functional gamma-probe shell emits a visible spot that marks the intersection, paired laser-on/laser-off stereo images supply ground truth labels, and a two-branch regression network (ResNet for image features, MLP for 50 PCA-sampled principal-axis points) is trained end-to-end with mean squared error loss on laser-off images. Segmentation baselines fail because they depend on the laser spot being present; the regression approach succeeds because the network learns scene-level cues from the image and probe geometry. On the two newly acquired datasets the ResNet+MLP combination reports a mean 2D error around 70 pixels, an $R^2$ of 0.82, and 50 fps inference, which the thesis describes as a new benchmark for the surgical vision community.

Load-bearing premise

The mock probe's laser beam marks exactly the same tissue point that the real gamma probe would sense, and a network trained on 120 silicone-phantom poses with that laser label will transfer to real tissue and real probe geometry.

Editorial extensions

If this is right

If the central claim holds, intraoperative AR overlays showing the gamma probe's sensing area can be generated from a standard RGB laparoscope feed at 50 fps, with no extra tracking hardware.
Surgeons would no longer need to memorise count readings; a visible cue on tissue could reduce incomplete resections and unnecessary dissection of healthy lymph nodes.
The paired laser-on/laser-off protocol provides a practical way to generate intersection-point ground truth for other non-imaging probes, such as ultrasound or diffuse reflectance spectroscopy probes.
The two released datasets give other groups a common benchmark to compare sensing-area detection algorithms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Extension: a decisive test not performed in the thesis would use a real gamma probe with a buried radioactive source and compare the predicted sensing point with the point of maximum gamma count; if the laser axis and gamma detector axis are not co-aligned in the clinical device, the reported accuracy will not transfer.
Extension: the phantom-to-patient gap remains the main open question, since silicone tissue with hand-painted colour has more texture than many in-vivo scenes and PCA-based axis extraction may degrade on low-texture, specular, or blood-covered tissue; evaluating on in-vivo or ex-vivo video with laser labels would bound the domain shift.
Extension: because the network takes the 2D probe axis as input, a tracking failure that slightly rotates the axis estimate will propagate directly into the intersection prediction; fusing kinematic data from the robotic arm could stabilise this in practice, an option the thesis mentions only as future work.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The core idea in Chapter 7 is real: casting gamma probe sensing area detection as regression from stereo RGB plus sampled probe-axis points, trained with laser-derived labels, is not something I've seen elsewhere. The hardware setup (stereo laparoscope, rotation stage, laser mock probe, shutter control) is thoughtful, and the two datasets, if actually released, could be genuinely useful to the surgical vision community. The earlier depth-estimation chapters are competent Monodepth-style extensions, already peer-reviewed at MICCAI and T-MRB; they're not the main event but they hold up.

The soft spot is the validation of the central claim. Section 7.4.4 says the 1200 images (120 camera-probe poses x 10 phantom surface rotations) are partitioned into 800/200/200 without any mention of stratifying by pose. If that split is random at image level, then each pose appears in both training and test, and the reported 70.5-pixel mean error / R2=0.82 mostly measure interpolation across the 10 surface rotations of a fixed pose. That is not the same as generalizing to unseen camera-probe poses, which is what the 'new benchmark' language implies. The stress-test note is right to flag this. I'm not claiming the method is bad; I'm claiming the reported numbers don't yet mean what the abstract says they mean.

There is also no quantitative comparison to the geometric tracking and SfM baseline described in Chapter 3. That is the natural null method to beat, and its absence is conspicuous. The validation is phantom-only, with a mock laser probe; the alignment between laser spot and the real gamma probe's sensing axis is never calibrated. Those limitations are not fatal for a conference-grade contribution, but they further cap what the current evidence supports.

Minor stuff: the code/data link is promised but not actually present in the arXiv rendering, and at least two reference labels look wrong. Both are easy fixes.

Who gets value from this: researchers working on surgical instrument guidance and intraoperative visualization, especially anyone who wants to pick up the Jerry/Coffbee datasets for the new task. The problem formulation is worth having in the literature.

Recommendation: don't desk reject. Send it to peer review with explicit reviewer instructions to demand a pose-disjoint evaluation and a comparison against the geometric baseline. If those are added, the benchmark claim becomes testable; without them, the abstract's 'new benchmark' is unsupported.

Referee Report

4 major / 4 minor

Summary. This PhD thesis addresses the intraoperative visualisation of a tethered laparoscopic gamma probe (SENSEI) in minimally invasive cancer surgery. It develops marker-based probe tracking with augmented reality (Chapter 3), self-supervised depth estimation methods SADepth and M3Depth (Chapters 4 and 5), joint depth estimation and surgical tool segmentation (Chapter 6), and a new sensing-area detection method based on a laser-instrumented mock probe and a regression network (Chapter 7). The central claim of Chapter 7 is that a simple ResNet-plus-MLP network can predict the probe-tissue intersection point in real time from standard RGB stereo images together with PCA-sampled probe-axis points, reporting a best 2D mean error of 70.5 pixels, a 3D mean error of 7.4 mm, an R2 score of 0.82, and an inference rate of 50 frames per second, and describing this as a new benchmark for the surgical vision community.

Significance. The thesis contains three previously published method chapters and a new application chapter. Its strongest assets are the problem reformulation that turns the sensing-area localisation task into a 2D regression problem, the custom hardware platform with structured-light depth references, and the public release of the Jerry and Coffbee datasets. If the reported accuracy held under a pose-disjoint evaluation, the simple ResNet-plus-MLP architecture at 50 fps would be a practically useful contribution to intraoperative visualisation. However, the evaluation as presented does not yet establish generalisation to unseen camera-probe poses, and the claimed 'new benchmark' is therefore premature.

major comments (4)

[§7.3, §7.4.4] The evaluation protocol does not support the generalisation claim. Section 7.3 states that each of the 120 camera-probe poses is imaged under 10 phantom surface profiles, yielding 1200 images, and Section 7.4.4 partitions the Jerry and Coffbee datasets into 800/200/200 images without any statement of stratification by pose or surface profile. With an image-level random split, frames from the same pose can appear in both training and test, so the reported 70.5 px mean error and R2=0.82 may reflect interpolation across the 10 rotations of a seen pose rather than detection on unseen poses. Please report results with a pose-disjoint split, for example training on 100 poses and testing on the remaining 20 poses, and state whether the 70.5 px figure changes.
[§7.4.1, §7.5, Table 7.2] The claimed 'new benchmark' is not supported without a baseline comparison to the geometric tracking and SfM intersection method of Chapter 3. Section 7.4.1 rejects that approach on practical grounds such as sterilisation and the need to move the laparoscope, but it provides no quantitative comparison. Since Chapter 3 already reports pose-estimation errors and an AR intersection demonstration, the sensing-area task can be evaluated against that geometric approach using the same error metric. Please add this comparison, and ideally also the segmentation baselines of Table 7.1 under the same test protocol.
[§7.3, §7.4.3] The laser module is used to define ground truth for the gamma probe sensing area, but the manuscript reports no calibration of the alignment between the laser beam axis and the SENSEI probe axis, and no sensitivity analysis of how laser-spot-centre errors propagate to the regression labels. Because the laser spot centroid is the training target, any laser-to-axis misalignment in the mock probe or in a real probe corrupts both training and test labels. Please report the calibration procedure and, ideally, a tolerance analysis.
[§7.5, Table 7.2] The text states that 'LSTM and MLP gave competitive results and they are all in sub-millimetre level,' but Table 7.2 lists 3D mean errors of 6.4-11.2 mm and medians of 4.0-5.4 mm. This is an internal inconsistency that should be corrected. The 3D error metric should also be described more carefully given the depth-map resolution and missing-depth limitations noted in the same section.

minor comments (4)

[Tables 7.2 and 7.3] The R2 score and the 3D error rows are not explicitly associated with the grey (Jerry) and blue (Coffbee) dataset blocks; please clarify the table layout in the caption.
[§7.4.1] The statement that the method 'relies solely on the 2D information and works well without the need for the laser module after training' is not accompanied by a failure-mode analysis; consider reporting cases where PCA-based axis extraction fails, such as low-texture tissue or partial probe occlusion.
[Eq. (5.7)] The notation in Eq. (5.7) is inconsistent: the subscripts and superscripts of L^{2D(l)}_{lr}, L^{2D(r)}_{lr}, and L^{3D}_{gc} are not defined uniformly, and L^{3D}_{lr} appears without a definition.
[Chapter 7] The text says 'Code and data are available at this link,' but no URL or repository identifier appears in the manuscript; please provide the actual link.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central sensing-area regression is supervised by laser-derived labels on a held-out split, and the depth-estimation chapters are self-supervised against photometric/geometric losses; self-citations are descriptive, not load-bearing.

full rationale

The thesis's central claim in Chapter 7 is that a simple regression network can infer the probe axis–tissue intersection from standard RGB images plus PCA-sampled probe-axis points, using laser-spot centers as ground truth. This is a standard supervised learning setup: training labels come from a paired laser-on image, while test inputs are laser-off images (§7.4.3). The network does not receive the laser spot at test time, and the reported errors are computed on a partition of the newly released Jerry/Coffbee datasets (§7.4.4). There is no equation in which the predicted quantity is defined as the fitted parameter, and no fitted value is renamed as a prediction. The depth-estimation chapters (SADepth, M3Depth, SDSNET) use self-supervised photometric reprojection, disparity smoothness, and 3D geometric consistency losses, and are evaluated on public SCARED/dVPN and the authors' LATTE datasets; these are independent checks, not reductions to the loss definitions. Some self-citations appear ([3], [4], [6], [9] in §7.2; the LATTE structured-light method in §5.3.1; the IJCARS/MICCAI papers for Chapters 3–6), but they describe prior methods or datasets and are not invoked as a uniqueness theorem or as the sole justification for the new benchmark. The only concerning passage is §7.4.4, which states only that the Jerry dataset was 'partitioned' into 800/200/200 images, without specifying a pose-disjoint split. Given that the dataset was built from 120 camera-probe poses with 10 phantom profiles each (§7.3), a random image-level split could place the same pose in both training and test, inflating apparent generalization. However, the text does not specify the split mechanism, so this is a potential evaluation-leakage risk rather than a demonstrated circular reduction of the derivation chain. Under the hard rule requiring a quoted reduction, no circular step is established.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on domain-specific assumptions about the probe geometry, the laser proxy, and phantom-to-real transfer. The free parameters are training hyperparameters and depth scaling choices, none of which are fitted to the target sensing area labels. No new theoretical entities are introduced.

free parameters (5)

Depth range parameters a, b (SADepth/SDSNET) = not specified (constrain depth to 0.1-100 units)
Chosen by hand to convert sigmoid disparity to depth scale; affects depth output but not the central sensing area claim.
ICP loss weight beta (M3Depth) = 0.001
Hand-set to balance 3D geometric consistency loss against 2D losses; not fitted to the target data.
Appearance loss weight gamma = 0.85
Standard weighting between SSIM and L1 in self-supervised depth; hand-set and used in prior work.
Number of sampled probe-axis points = 50
Chosen for the regression input in Chapter 7; a design choice that may affect accuracy.
Multi-scale output count m = 4
SADepth uses 4 decoder scales; experimental setting.

assumptions (5)

domain assumption The sensing area is defined as the intersection point between the gamma probe axis and the tissue surface.
Used in Chapter 2.3 and 7.1 to define the output of the system; a point, rather than a finite detection cone.
ad hoc to paper The laser spot center in the mock probe is an accurate proxy for the gamma probe sensing area.
Chapter 7.1 states the laser module 'clearly optically indicate[s] the sensing area'; this alignment is not verified against a real gamma detector.
domain assumption The PCA-extracted probe axis from 2D images is a reliable input to the network.
Chapter 7.4.3 uses PCA on detected features to sample 50 points along the axis; if axis estimation fails in real surgery, the regression input is corrupted.
domain assumption Silicone phantom tissue is representative of real tissue appearance for training.
Chapter 7.3 describes a hand-painted silicone phantom; generalization to in-vivo tissue is not demonstrated.
domain assumption Photometric consistency between stereo views is valid for self-supervised depth training.
Chapters 4 and 5 rely on the standard assumption of Lambertian surfaces and constant illumination, which is violated by specular reflection and tissue deformation.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery." pith.science (2026). https://pith.science/paper/GBGOAQSO

@misc{pith2026250101752,
  author       = {Pith},
  title        = {Pith review of: Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GBGOAQSO}},
  note         = {Machine review of arXiv:2501.01752}
}

read the original abstract

Cancer remains a significant health challenge worldwide, with a new diagnosis occurring every two minutes in the UK. Surgery is one of the main treatment options for cancer. However, surgeons rely on the sense of touch and naked eye with limited use of pre-operative image data to directly guide the excision of cancerous tissues and metastases due to the lack of reliable intraoperative visualisation tools. This leads to increased costs and harm to the patient where the cancer is removed with positive margins, or where other critical structures are unintentionally impacted. There is therefore a pressing need for more reliable and accurate intraoperative visualisation tools for minimally invasive surgery to improve surgical outcomes and enhance patient care. A recent miniaturised cancer detection probe (i.e., SENSEI developed by Lightpoint Medical Ltd.) leverages the cancer-targeting ability of nuclear agents to more accurately identify cancer intra-operatively using the emitted gamma signal. However, the use of this probe presents a visualisation challenge as the probe is non-imaging and is air-gapped from the tissue, making it challenging for the surgeon to locate the probe-sensing area on the tissue surface. Geometrically, the sensing area is defined as the intersection point between the gamma probe axis and the tissue surface in 3D space but projected onto the 2D laparoscopic image. Hence, in this thesis, tool tracking, pose estimation, and segmentation tools were developed first, followed by laparoscope image depth estimation algorithms and 3D reconstruction methods.

Figures

Figures reproduced from arXiv: 2501.01752 by the authors.

**Figure 2.1.** The SENSEI® probe. Image courtesy of Lightpoint Medical Ltd. rigid, hand-held probes. It enables high-sensitivity detection of gamma imaging agents that are administered to the patient prior to surgery and selectively accumulate in cancerous tissue, by providing an audible and numerical read-out ( [PITH_FULL_IMAGE:figures/full_fig_p042_2_1.png] view at source ↗

**Figure 3.1.** (a) An example of a tethered probe being used in MIS; (b) The gamma probe marker; (c) Example detected circular dots and chessboard vertices; (d) The local coordinates defined on the probe In this work, we proposed a dual-pattern marker ( [PITH_FULL_IMAGE:figures/full_fig_p060_3_1.png] view at source ↗

**Figure 3.2.** Feature detection algorithm workflow The detection of the proposed marker consisted of two parts: blob detection and chessboard vertices detection. The detection algorithm workflow is shown in [PITH_FULL_IMAGE:figures/full_fig_p061_3_2.png] view at source ↗

Figures from the paper (18 more)

**Figure 3.3.** Figure 3.3: (a) Hardware set-up for experiments; (b) The transformation matrices between laparoscope, OptiTrack system, optical sensors, and designed marker [PITH_FULL_IMAGE:figures/full_fig_p065_3_3.png]

**Figure 3.4.** Figure 3.4: (a) Successful marker tracking in the case of occlusion; (b) The experimental visualisation results for different testing distances (Near: 50- 100mm, Middle: 100-150mm, far: 150-200mm) between the probe and camera. 3.3.3 Projection error Given the geometric parameter…

**Figure 3.5.** Figure 3.5: Examples where the pose estimation is more accurate by using (a) the circular dots pattern; (b) the chessboard vertices; Example where tracking failed for (c) the circular dots pattern and (d) the chessboard vertices. In (e) both vertices and dots pattern are detecte…

**Figure 3.6.** Figure 3.6: The hardware setup including laparoscope, image monitor, prostate phantom, SENSEI® probe, and control unit showing (a, b) a higher radiation level when the probe was pointing to and placed closer to the radioactive source; and (c, d) a lower radiation level when the …

**Figure 4.1.** Figure 4.1: Overview of the self-supervised adversarial depth estimation network, SADepth. lar to [140], our decoder was formed of five cascaded blocks where each block had four parts: the first convolutional layer, an upsampling layer, a concatenation manipulation, and the se…

**Figure 4.2.** Figure 4.2: The detailed architecture of the SADepth generator and discriminator. The generator was an autoencoder architecture with concatenated stereo image pairs as inputs and left and right disparity maps as outputs using a sigmoid function. These outputs were then transfor…

**Figure 4.3.** Figure 4.3: Qualitative results on dVPN dataset. From left to right, they are left image, right image, right depth map, and reconstructed right image. Training The depth estimation procedure was trained based on the reconstruction supervision signal and no per-pixel depth groun…

**Figure 5.1.** Figure 5.1: Overview of the proposed self-supervised depth estimation network. ResNet18 was adopted as the backbone and received a left image from a stereo image pair as the input. Left and right disparity maps were produced simultaneously and formed 2D losses with the original…

**Figure 5.2.** Figure 5.2: The data acquisition setting and examples of the LATTE dataset. T = 2 √ 2 3 × p (I1 − I2) 2 + (I2 − I3) 2 + (I1 − I3) 2 (5.8) where I1, I2, I3 denotes original modulation and modulations after shifts. 5.3.2 Evaluation Metrics, Baseline, and Implementation Evaluation …

**Figure 5.3.** Figure 5.3: Qualitative results on the SCARED dataset with error map of M3Depth. The depth predictions are all for the left input image. M3Depth generated depth maps with high contrast between the foreground and background and performed better at distinguishing different parts …

**Figure 6.1.** Figure 6.1: The detailed architecture of SDSNET. The depth branch and the segmentation branch share the same encoder network. The features of the third convolutional layer in the segmentation branch decoder are fused with the features from the fourth block in the depth branch de…

**Figure 6.2.** Figure 6.2: Qualitative results on stereo pairs. (a) and (b) from top to bottom: the left input image, the corresponding right image in the stereo pair, the predicted depth map, and the segmentation result from SDSNET. Baoru Huang Gamma Probe for MIS 118 [PITH_FULL_IMAGE:figur…

**Figure 7.1.** Figure 7.1: (a) Hardware set-up for experiments, including a customised portable stereo laparoscope system and the SENSEI® probe, a rotation stage, a laparoscopic lighting source, and a phantom; (b) An example of the use of the SENSEI® probe in MIS. The cancer detection probe SE…

**Figure 7.2.** Figure 7.2: shows a sample from our dataset. (a) (b) (c) Laser spot Laser spot (d) Left image Left image Right image Right image [PITH_FULL_IMAGE:figures/full_fig_p130_7_2.png]

**Figure 7.3.** Figure 7.3: The structure lighting patterns and example depth maps. The top row shows the left camera. The bottom row shows the right camera. All data acquisition and devices were controlled by Python and LABVIEW programs, and complete data sets of the above images were collecte…

**Figure 7.4.** Figure 7.4: Sensing area detection. (a) The input RGB image, (b) The estimated line using PCA for obtaining principal points, (c) The image with laser on that we used to detect the intersection ground truth. Images ResBlock1 .... ResBlock4 FC Concatenation MLP1 MLP2 Loss Feature…

**Figure 7.5.** Figure 7.5: An overview of our approach using ResNet and MLP. goal was to predict the intersection point Pintersect on the surface of the tissue. During the training, the ground truth intersection point position was provided by the laser source, while during testing the intersec…

**Figure 7.6.** Figure 7.6: Qualitative results. (a) and (c) are standard illumination images and (b) and (d) are images with laser on and laparoscopic light off. The predicted intersection point is shown in blue and the green point indicates the ground truth, which are further indicated by arr…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

199 extracted references · 52 canonical work pages

[1]

Robinson

Robert D. Robinson. Reliable Machine Learning for Medical Imaging Data through Automated Quality Control and Data Harmonization. 7 2020

2020
[2]

Detecting the sensing area of a laparoscopic probe in mini- mally invasive cancer surgery

Baoru Huang, Yicheng Hu, Anh Nguyen, Stamatia Giannarou, and Daniel S Elson. Detecting the sensing area of a laparoscopic probe in mini- mally invasive cancer surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention , pages 260–270. Springer, 2023

2023
[3]

Self- supervised depth estimation in laparoscopic image using 3d geometric consistency

Baoru Huang, Jian-Qing Zheng, Anh Nguyen, Chi Xu, Ioannis Gkouzionis, Kunal Vyas, David Tuch, Stamatia Giannarou, and Daniel S Elson. Self- supervised depth estimation in laparoscopic image using 3d geometric consistency. In Medical Image Computing and Computer Assisted Inter- vention, 2022

2022
[4]

Self-supervised generative adver- sarial network for depth estimation in laparoscopic images

Baoru Huang, Jian-Qing Zheng, Anh Nguyen, David Tuch, Kunal Vyas, Sta- matia Giannarou, and Daniel S Elson. Self-supervised generative adver- sarial network for depth estimation in laparoscopic images. In Medi- cal Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 202...

2021
[5]

Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe

Baoru Huang, Ya-Yen Tsai, Jo ˜ao Cartucho, Kunal Vyas, David Tuch, Sta- matia Giannarou, and Daniel S Elson. Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe. International 146 BIBLIOGRAPHY BIBLIOGRAPHY Journal of Computer Assisted Radiology and Surgery , 15(8):1389–1397, 2020

2020
[6]

Simultaneous depth estimation and surgical tool segmentation in laparoscopic images

Baoru Huang, Anh Nguyen, Siyao Wang, Ziyang Wang, Erik Mayer, David Tuch, Kunal Vyas, Stamatia Giannarou, and Daniel S Elson. Simultaneous depth estimation and surgical tool segmentation in laparoscopic images. IEEE Transactions on Medical Robotics and Bionics, 4(2):335–338, 2022

2022
[7]

Residual aligner-based network (ran): Motion- separable structure for coarse-to-ﬁne discontinuous deformable registra- tion

Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, and Bart lomiej W Papie ˙z. Residual aligner-based network (ran): Motion- separable structure for coarse-to-ﬁne discontinuous deformable registra- tion. Medical Image Analysis, 91:103038, 2024

2024
[8]

Deep imitation learning for automated drop-in gamma probe manipulation

Kaizhong Deng, Baoru Huang, and Daniel S Elson. Deep imitation learning for automated drop-in gamma probe manipulation. Hamlyn Symposium 2023, 2023

2023

Show all 199 references

[9]

Self-supervised monocular depth estimation with 3-d displacement module for laparoscopic images

Chi Xu, Baoru Huang, and Daniel S Elson. Self-supervised monocular depth estimation with 3-d displacement module for laparoscopic images. IEEE transactions on medical robotics and bionics, 4(2):331–334, 2022

2022
[10]

Elson, Ara Darzi, and Stamatia Giannarou

Jo ˜ao Cartucho, Chiyu Wang, Baoru Huang, Dan S. Elson, Ara Darzi, and Stamatia Giannarou. An enhanced marker pattern that achieves improved accuracy in surgical tool tracking. Computer Methods in Biomechan- ics and Biomedical Engineering: Imaging & Visualization , 10(4):400–408, 2022

2022
[11]

Two hundred years of cancer research

Vincent T DeVita Jr and Steven A Rosenberg. Two hundred years of cancer research. New England Journal of Medicine , 366(23):2207–2214, 2012

2012
[12]

Laparoscopic vs open surgery: a preliminary comparison of quality-of-life outcomes

Vic Velanovich. Laparoscopic vs open surgery: a preliminary comparison of quality-of-life outcomes. Surgical endoscopy, 14:16–21, 2000. Baoru Huang Gamma Probe for MIS 147 BIBLIOGRAPHY BIBLIOGRAPHY

2000
[13]

La- paroscopic versus open surgery for suspected appendicitis

Stefan Sauerland, Thomas Jaschinski, and Edmund AM Neugebauer. La- paroscopic versus open surgery for suspected appendicitis. Cochrane Database of Systematic Reviews, (10), 2010

2010
[14]

Recent technical developments in the ﬁeld of laparo- scopic surgery: a literature review

Lama Islem Basunbul, Lenah Sulaiman S Alhazmi, Shahad Amro Al- mughamisi, Najd Muhammed Aljuaid, Hisham Rizk, Rana Moshref, and Lenah Alhazmi. Recent technical developments in the ﬁeld of laparo- scopic surgery: a literature review. Cureus, 14(2), 2022

2022
[15]

Laparo- scopic gastric surgery for cancer: where do we stand? World Journal of Gastroenterology: WJG, 20(39):14280, 2014

Pantelis T Antonakis, Hutan Ashraﬁan, and Alberto Martinez Isla. Laparo- scopic gastric surgery for cancer: where do we stand? World Journal of Gastroenterology: WJG, 20(39):14280, 2014

2014
[16]

Laparoscopic surgery for gastric cancer: a collective review with meta-analysis of ran- domized trials

Yasuhiro Kodera, Michitaka Fujiwara, Norifumi Ohashi, Goro Nakayama, Masahiko Koike, Satoshi Morita, and Akimasa Nakao. Laparoscopic surgery for gastric cancer: a collective review with meta-analysis of ran- domized trials. Journal of the American College of Surgeons, 211(5):6...

2010
[17]

Transanal endoscopic surgery for rectal cancer.Surgery for Cancers of the Gastrointestinal Tract: A Step- by-Step Approach, pages 309–319, 2015

Xavier Serra-Aracil and Laura Mora-Lopez. Transanal endoscopic surgery for rectal cancer.Surgery for Cancers of the Gastrointestinal Tract: A Step- by-Step Approach, pages 309–319, 2015

2015
[18]

Endoscopic treatment of early cancer of the colon.Gastroenterology & hepatology, 11(7):445, 2015

Maria Sylvia Ribeiro and Michael B Wallace. Endoscopic treatment of early cancer of the colon.Gastroenterology & hepatology, 11(7):445, 2015

2015
[19]

Natural oriﬁce transvaginal endoscopic surgery for endometrial cancer

Chyi-Long Lee, Kai-Yun Wu, Fang-Ying Tsao, Chen-Ying Huang, Chien-Min Han, Chih-Feng Yen, and Kuan-Gen Huang. Natural oriﬁce transvaginal endoscopic surgery for endometrial cancer. Gynecology and Minimally Invasive Therapy, 3(3):89–92, 2014

2014
[20]

https:/ /www.cancerresearchuk.org/ health-professional/cancer-statistics-for-the-uk

Cancer statistics for the uk. https:/ /www.cancerresearchuk.org/ health-professional/cancer-statistics-for-the-uk. Accessed: 15-12-2023. Baoru Huang Gamma Probe for MIS 148 BIBLIOGRAPHY BIBLIOGRAPHY

2023
[21]

https:/ /www

Cost of preventable cancers in the uk to rise from £78bn in 2023 to £1.26 tn by 2040. https:/ /www. frontier-economics.com/uk/en/news-and-articles/news/ news-article-i20141-cost-of-preventable-cancers-in-the-uk-to-rise/. Accessed: 15-12-2023

2023
[22]

https:/ /www.nhs.uk/conditions/cancer/

Cost of preventable cancers in the uk to rise from £78bn in 2023 to £1.26 tn by 2040. https:/ /www.nhs.uk/conditions/cancer/. Accessed: 15-12- 2023

2023
[23]

Ghani, Marco Bianchi, Wooju Jeong, Shahrokh F

Quoc-Dien Trinh, Jesse Sammon, Maxine Sun, Praful Ravi, Khurshid R. Ghani, Marco Bianchi, Wooju Jeong, Shahrokh F . Shariat, Jens Hansen, Jan Schmitges, Claudio Jeldres, Craig G. Rogers, James O. Peabody, Francesco Montorsi, Mani Menon, and Pierre I. Karakiewicz. Periop- erati...

2012
[24]

Positive surgical margin at radical prostatectomy: futile or surgeon-dependent predictor of prostate cancer death

Martin Spahn and Steven Joniau. Positive surgical margin at radical prostatectomy: futile or surgeon-dependent predictor of prostate cancer death. Eur Urol, 64(1):26–8, 2013

2013
[25]

Avoidance and management of positive surgical margins before, during and after radical prostatectomy

S R J Bott and R S Kirby. Avoidance and management of positive surgical margins before, during and after radical prostatectomy. Prostate Cancer and Prostatic Diseases, 5(4):252–263, 12 2002

2002
[26]

Nerve-sparing surgery technique, not the preservation of the neurovascular bundles, leads to improved long-term continence rates after radical prostatectomy

Uwe Michl, Pierre Tennstedt, Lena Feldmeier, Philipp Mandel, Su J Oh, Sascha Ahyai, Lars Bud ¨aus, Felix KH Chun, Alexander Haese, Hans Heinzer, et al. Nerve-sparing surgery technique, not the preservation of the neurovascular bundles, leads to improved long-term continence ra...

2016
[27]

Long-term outcome after radical prostatectomy for patients with lymph node positive prostate cancer in the prostate speciﬁc antigen era

Stephen A Boorjian, R Houston Thompson, Sameer Siddiqui, Stephanie Bagniewski, Erik J Bergstralh, R Jeffrey Karnes, Igor Frank, and Michael L Blute. Long-term outcome after radical prostatectomy for patients with lymph node positive prostate cancer in the prostate speciﬁc anti...

2007
[28]

Cumberbatch, Maria De Santis, Nicola Fossati, Tobias Gross, Ann M

Nicolas Mottet, Joaquim Bellmunt, Michel Bolla, Erik Briers, Marcus G. Cumberbatch, Maria De Santis, Nicola Fossati, Tobias Gross, Ann M. Henry, Steven Joniau, Thomas B. Lam, Malcolm D. Mason, Vsevolod B. Matveev, Paul C. Moldovan, Roderick C.N. van den Bergh, Thomas Van den B...

2017
[29]

Prostate-speciﬁc membrane antigen– radioguided surgery for metastatic lymph nodes in prostate cancer

Tobias Maurer, Gregor Weirich, Margret Schottelius, Martina Weineisen, Benjamin Frisch, Asli Okur, Hubert K ¨ubler, Mark Thalgott, Nassir Navab, Markus Schwaiger, et al. Prostate-speciﬁc membrane antigen– radioguided surgery for metastatic lymph nodes in prostate cancer. Eu- r...

2015
[30]

Prostate-speciﬁc membrane antigen expression in normal and malignant human tissues

David A Silver, Inmaculada Pellicer, William R Fair, WD Heston, and Carlos Cordon-Cardo. Prostate-speciﬁc membrane antigen expression in normal and malignant human tissues. Clinical cancer research: an ofﬁcial journal of the American Association for Cancer Research , 3(1):81–85, 1997

1997
[31]

Prostate-speciﬁc membrane antigen expression as a pre- Baoru Huang Gamma Probe for MIS 150 BIBLIOGRAPHY BIBLIOGRAPHY dictor of prostate cancer progression

Sven Perner, Matthias D Hofer, Robert Kim, Rajal B Shah, Haojie Li, Pe- ter M¨oller, Richard E Hautmann, Juergen E Gschwend, Rainer Kuefer, and Mark A Rubin. Prostate-speciﬁc membrane antigen expression as a pre- Baoru Huang Gamma Probe for MIS 150 BIBLIOGRAPHY BIBLIOGRAPHY di...

2007
[32]

A novel cytoplasmic tail mxxxl motif mediates the internal- ization of prostate-speciﬁc membrane antigen

Sigrid A Rajasekaran, Gopalakrishnapillai Anilkumar, Eri Oshima, James U Bowie, He Liu, Warren Heston, Neil H Bander, and Ayyappan K Ra- jasekaran. A novel cytoplasmic tail mxxxl motif mediates the internal- ization of prostate-speciﬁc membrane antigen. Molecular biology of th...

2003
[33]

68ga-psma-11 pet/ct inter- observer agreement for prostate cancer assessments: an international multicenter prospective study

Wolfgang Peter Fendler, Jeremie Calais, Martin Allen-Auerbach, Christina Bluemel, Nina Eberhardt, Louise Emmett, Pawan Gupta, Markus Harten- bach, Thomas A Hope, Shozo Okamoto, et al. 68ga-psma-11 pet/ct inter- observer agreement for prostate cancer assessments: an internation...

2017
[34]

First experience with spect/ct using a 99mtc- labeled inhibitor for prostate-speciﬁc membrane antigen in patients with biochemical recurrence of prostate cancer

Julia Reinfelder, Torsten Kuwert, Michael Beck, James C Sanders, Philipp Ritt, Christian Schmidkonz, Peter Hennig, Olaf Prante, Michael Uder, Bernd Wullich, et al. First experience with spect/ct using a 99mtc- labeled inhibitor for prostate-speciﬁc membrane antigen in patients...

2017
[35]

Spect/ct with the psma ligand 99mtc-mip-1404 for whole-body primary staging of patients with prostate cancer

Christian Schmidkonz, Michael Cordes, Michael Beck, Theresa Ida Goetz, Daniela Schmidt, Olaf Prante, Tobias B ¨auerle, Michael Uder, Bernd Wul- lich, Peter Goebell, et al. Spect/ct with the psma ligand 99mtc-mip-1404 for whole-body primary staging of patients with prostate can...

2018
[36]

Karolien E Gofﬁn, Steven Joniau, Peter Tenke, Kevin Slawin, Eric A Klein, Nancy Stambler, Thomas Strack, John Babich, Thomas Armor, and Vivien Baoru Huang Gamma Probe for MIS 151 BIBLIOGRAPHY BIBLIOGRAPHY Wong. Phase 2 study of 99mtc-trofolastat spect/ct to identify and local-...

2017
[37]

Isabel Rauscher, Charlotte D ¨uwel, Martina Wirtz, Margret Schottelius, Hans-J¨urgen Wester, Kristina Schwamborn, Bernhard Haller, Markus Schwaiger, J ¨urgen E Gschwend, Matthias Eiber, et al. Value of 111in- prostate-speciﬁc membrane antigen (psma)-radioguided surgery for sal...

2017
[38]

Maximizing the beneﬁt of min- imally invasive surgery

Kamran Mohiuddin and Scott J Swanson. Maximizing the beneﬁt of min- imally invasive surgery. Journal of surgical oncology , 108(5):315–319, 2013

2013
[39]

Three-dimensional laparoscopy: principles and practice

Rakesh Y Sinha, Shweta R Raje, and Gayatri A Rao. Three-dimensional laparoscopy: principles and practice. Journal of minimal access surgery, 13(3):165, 2017

2017
[40]

Intraoperative ultrasound overlay in robot-assisted partial nephrectomy: ﬁrst clinical experience

Archie Hughes-Hallett, Philip Pratt, Erik Mayer, Aimee Di Marco, Guang- Zhong Yang, Justin Vale, and Ara Darzi. Intraoperative ultrasound overlay in robot-assisted partial nephrectomy: ﬁrst clinical experience. 2013

2013
[41]

Intraoperative reg- istered transrectal ultrasound guidance for robot-assisted laparoscopic radical prostatectomy

Omid Mohareri, Joseph Ischia, Peter C Black, Caitlin Schneider, Julio Lobo, Larry Goldenberg, and Septimiu E Salcudean. Intraoperative reg- istered transrectal ultrasound guidance for robot-assisted laparoscopic radical prostatectomy. The Journal of urology, 193(1):302–312, 2015

2015
[42]

Real- time surgical tool tracking and pose estimation using a hybrid cylindrical Baoru Huang Gamma Probe for MIS 152 BIBLIOGRAPHY BIBLIOGRAPHY marker

Lin Zhang, Menglong Ye, Po-Ling Chan, and Guang-Zhong Yang. Real- time surgical tool tracking and pose estimation using a hybrid cylindrical Baoru Huang Gamma Probe for MIS 152 BIBLIOGRAPHY BIBLIOGRAPHY marker. International journal of computer assisted radiology and surgery ,...

2017
[43]

Magneto-optic tracking of a ﬂexible laparo- scopic ultrasound transducer for laparoscope augmentation

Marco Feuerstein, Tobias Reichl, Jakob Vogel, Armin Schneider, Hubertus Feussner, and Nassir Navab. Magneto-optic tracking of a ﬂexible laparo- scopic ultrasound transducer for laparoscope augmentation. In Medi- cal Image Computing and Computer-Assisted Intervention–MICCAI 200...

2007
[44]

Intraoperative ultrasound guidance for transanal en- doscopic microsurgery

Philip Pratt, Aimee Di Marco, Christopher Payne, Ara Darzi, and Guang- Zhong Yang. Intraoperative ultrasound guidance for transanal en- doscopic microsurgery. In Medical Image Computing and Computer- Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, Franc...

2012
[45]

Calibration and stereo tracking of a laparoscopic ultrasound transducer for aug- mented reality in surgery

Philip Edgcumbe, Christopher Nguan, and Robert Rohling. Calibration and stereo tracking of a laparoscopic ultrasound transducer for aug- mented reality in surgery. In Augmented Reality Environments for Medi- cal Imaging and Computer-Assisted Interventions: 6th International Wo...

2013
[46]

Robust intraoperative us probe tracking using a monocular en- doscopic camera

Uditha L Jayarathne, A Jonathan McLeod, Terry M Peters, and Elvis CS Chen. Robust intraoperative us probe tracking using a monocular en- doscopic camera. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, Se...

2013
[47]

Robust ultrasound probe tracking: initial clinical experiences during robot-assisted partial nephrectomy

Philip Pratt, Alexander Jaeger, Archie Hughes-Hallett, Erik Mayer, Justin Vale, Ara Darzi, Terry Peters, and Guang-Zhong Yang. Robust ultrasound probe tracking: initial clinical experiences during robot-assisted partial nephrectomy. International journal of computer assisted r...

1905
[48]

Data-driven visual tracking in retinal microsurgery

Raphael Sznitman, Karim Ali, Rogerio Richa, Russell H Taylor, Gregory D Hager, and Pascal Fua. Data-driven visual tracking in retinal microsurgery. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, France, October 1...

2012
[49]

Can masses of non-experts train highly accurate image classiﬁers? a crowdsourcing approach to instru- ment segmentation in laparoscopic images

Lena Maier-Hein, Sven Mersmann, Daniel Kondermann, Sebastian Boden- stedt, Alexandro Sanchez, Christian Stock, Hannes Gotz Kenngott, Math- ias Eisenmann, and Stefanie Speidel. Can masses of non-experts train highly accurate image classiﬁers? a crowdsourcing approach to instru-...

2014
[50]

Detecting surgical tools by modelling local appearance and global shape

David Bouget, Rodrigo Benenson, Mohamed Omran, Laurent Riffaud, Bernt Schiele, and Pierre Jannin. Detecting surgical tools by modelling local appearance and global shape. IEEE transactions on medical imag- ing, 34(12):2603–2617, 2015

2015
[51]

Articulated multi-instrument 2-d pose estimation using fully convolu- tional networks

Xiaofei Du, Thomas Kurmann, Ping-Lin Chang, Maximilian Allan, Se- bastien Ourselin, Raphael Sznitman, John D Kelly, and Danail Stoyanov. Articulated multi-instrument 2-d pose estimation using fully convolu- tional networks. IEEE transactions on medical imaging, 37(5):1276–1287...

2018
[52]

2017 robotic instrument segmentation challenge.arXiv preprint arXiv:1902.06426, 2019

Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, et al. 2017 robotic instrument segmentation challenge.arXiv preprint arXiv:1902.06426, 2019

2017 arXiv
[53]

2018 robotic scene segmen- tation challenge

Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, et al. 2018 robotic scene segmen- tation challenge. arXiv preprint arXiv:2001.11190, 2020

2018 arXiv
[54]

Robust medical instrument segmenta- tion challenge 2019

Tobias Ross, Annika Reinke, Peter M Full, Martin Wagner, Hannes Ken- ngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, et al. Robust medical instrument segmenta- tion challenge 2019. arXiv preprint arXiv:2003.10299, 2020

2019 arXiv
[55]

Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A Hicks, Vajira Tham- bawita, Enrique Garcia-Ceja, Michael A Riegler, Thomas de Lange, Peter T Schmidt, H ˚avard D Johansen, et al. Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal en...

2021
[56]

Cadis: Cataract dataset for surgical rgb-image seg- mentation

Maria Grammatikopoulou, Evangello Flouty, Abdolrahim Kadkhodamo- hammadi, Gwenol ´e Quellec, Andre Chow, Jean Nehme, Imanol Luengo, and Danail Stoyanov. Cadis: Cataract dataset for surgical rgb-image seg- mentation. Medical Image Analysis, 71:102053, 2021

2021
[57]

Cholec- seg8k: a semantic segmentation dataset for laparoscopic cholecystec- tomy based on cholec80

W-Y Hong, C-L Kao, Y-H Kuo, J-R Wang, W-L Chang, and C-S Shih. Cholec- seg8k: a semantic segmentation dataset for laparoscopic cholecystec- tomy based on cholec80. arXiv preprint arXiv:2012.12453, 2020. Baoru Huang Gamma Probe for MIS 155 BIBLIOGRAPHY BIBLIOGRAPHY

2012 arXiv
[58]

Image compositing for segmentation of surgical tools without manual annotations

Luis C Garcia-Peraza-Herrera, Lucas Fidon, Claudia D’Ettorre, Danail Stoy- anov, Tom Vercauteren, and Sebastien Ourselin. Image compositing for segmentation of surgical tools without manual annotations. IEEE trans- actions on medical imaging, 40(5):1450–1460, 2021

2021
[59]

Surgical tool datasets for machine learning research: a survey

Mark Rodrigues, Michael Mayo, and Panos Patros. Surgical tool datasets for machine learning research: a survey. International Journal of Com- puter Vision, 130(9):2222–2248, 2022

2022
[60]

Cathaction: A benchmark for endovascular intervention understanding

Baoru Huang, Tuan Vo, Chayun Kongtongvattana, Giulio Dagnino, Dennis Kundrat, Wenqiang Chi, Mohamed Abdelaziz, Trevor Kwok, Tudor Jianu, Tuong Do, et al. Cathaction: A benchmark for endovascular intervention understanding. arXiv preprint arXiv:2408.13126, 2024

2024 arXiv
[61]

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Comput- ing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Par...

2015
[62]

Mask r-cnn

Kaiming He, Georgia Gkioxari, Piotr Doll´ar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision , pages 2961–2969, 2017

2017
[63]

Can surgical simulation be used to train detection and classiﬁcation of neural networks? Healthcare technology letters, 4(5):216–222, 2017

Odysseas Zisimopoulos, Evangello Flouty, Mark Stacey, Sam Muscroft, Petros Giataganas, Jean Nehme, Andre Chow, and Danail Stoyanov. Can surgical simulation be used to train detection and classiﬁcation of neural networks? Healthcare technology letters, 4(5):216–222, 2017

2017
[64]

Very deep convolutional net- works for large-scale image recognition

Karen Simonyan and Andrew Zisserman. Very deep convolutional net- works for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014. Baoru Huang Gamma Probe for MIS 156 BIBLIOGRAPHY BIBLIOGRAPHY

2014 arXiv
[65]

Or-unet: an optimized robust residual u-net for instrument segmentation in endoscopic images

Fabian Isensee and Klaus H Maier-Hein. Or-unet: an optimized robust residual u-net for instrument segmentation in endoscopic images. arXiv preprint arXiv:2004.12668, 2020

2004 arXiv
[66]

Automatic instrument segmentation in robot-assisted surgery using deep learning

Alexey A Shvets, Alexander Rakhlin, Alexandr A Kalinin, and Vladimir I Iglovikov. Automatic instrument segmentation in robot-assisted surgery using deep learning. In 2018 17th IEEE international conference on ma- chine learning and applications (ICMLA), pages 624–628. IEEE, 2018

2018
[67]

U-netplus: A modiﬁed encoder- decoder u-net architecture for semantic and instance segmentation of surgical instruments from laparoscopic images

SM Kamrul Hasan and Cristian A Linte. U-netplus: A modiﬁed encoder- decoder u-net architecture for semantic and instance segmentation of surgical instruments from laparoscopic images. In 2019 41st annual in- ternational conference of the IEEE engineering in medicine and biolog...

2019
[68]

Streoscennet: surgical stereo robotic scene segmentation

Ahmed Mohammed, Sule Yildirim, Ivar Farup, Marius Pedersen, and Øis- tein Hovde. Streoscennet: surgical stereo robotic scene segmentation. In Medical imaging 2019: Image-guided procedures, robotic interventions, and modeling, volume 10951, pages 174–182. SPIE, 2019

2019
[69]

Isinet: an instance-based approach for surgical instrument segmentation

Cristina Gonz ´alez, Laura Bravo-S ´anchez, and Pablo Arbelaez. Isinet: an instance-based approach for surgical instrument segmentation. In Inter- national Conference on Medical Image Computing and Computer-Assisted Intervention, pages 595–605. Springer, 2020

2020
[70]

Real-time segmentation of surgical tools and needle using a mobile-u-net

Jakob Kristian Holm Andersen, Kim Lindberg Schwaner, and Thiusius Ra- jeeth Savarimuthu. Real-time segmentation of surgical tools and needle using a mobile-u-net. In 2021 20th International Conference on Advanced Robotics (ICAR), pages 148–154. IEEE, 2021

2021
[71]

Learning where to look while tracking instruments in robot-assisted surgery

Mobarakol Islam, Yueyuan Li, and Hongliang Ren. Learning where to look while tracking instruments in robot-assisted surgery. InInternational Con- Baoru Huang Gamma Probe for MIS 157 BIBLIOGRAPHY BIBLIOGRAPHY ference on Medical Image Computing and Computer-Assisted Intervention...

2019
[72]

Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions

Joonmyeong Choi, Sungman Cho, Jong Woo Chung, and Namkug Kim. Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions. Computer Methods and Programs in Biomedicine , 208:106251, 2021

2021
[73]

Yolov4: Optimal speed and accuracy of object detection

Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020

2004 arXiv
[74]

Pixel-based tool segmentation in cataract surgery videos with mask r-cnn

Markus Fox, Mario Taschwer, and Klaus Schoeffmann. Pixel-based tool segmentation in cataract surgery videos with mask r-cnn. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pages 565–568. IEEE, 2020

2020
[75]

Exploring deep learning methods for real-time surgical instrument segmentation in laparoscopy

Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Michael A Riegler, Dag Jo- hansen, H˚avard D Johansen, and P˚al Halvorsen. Exploring deep learning methods for real-time surgical instrument segmentation in laparoscopy. In 2021 IEEE EMBS International Conference on Biomedical and He...

2021
[76]

Semi-supervised se- mantic segmentation of cataract surgical images based on deeplab v3+

Hongyu Chen, Xiao Ma, Tong Xia, and Fucang Jia. Semi-supervised se- mantic segmentation of cataract surgical images based on deeplab v3+. In 2021 The 5th International Conference on Compute and Data Analysis , pages 112–118, 2021

2021
[77]

Assessing yolact++ for real time and robust instance segmen- tation of medical instruments in endoscopic procedures

Juan Carlos Angeles Cer ´on, Leonardo Chang, Gilberto Ochoa Ruiz, and Sharib Ali. Assessing yolact++ for real time and robust instance segmen- tation of medical instruments in endoscopic procedures. In 2021 43rd Baoru Huang Gamma Probe for MIS 158 BIBLIOGRAPHY BIBLIOGRAPHY Ann...

2021
[78]

Rasnet: Segmentation for tracking surgical in- struments in surgical videos using reﬁned attention segmentation net- work

Zhen-Liang Ni, Gui-Bin Bian, Xiao-Liang Xie, Zeng-Guang Hou, Xiao-Hu Zhou, and Yan-Jie Zhou. Rasnet: Segmentation for tracking surgical in- struments in surgical videos using reﬁned attention segmentation net- work. In 2019 41st annual international conference of the IEEE engi...

2019
[79]

Segmentation of surgical instruments in la- paroscopic videos: training dataset generation and deep-learning-based framework

Eung-Joo Lee, William Plishker, Xinyang Liu, Timothy Kane, Shuvra S Bhat- tacharyya, and Raj Shekhar. Segmentation of surgical instruments in la- paroscopic videos: training dataset generation and deep-learning-based framework. In Medical Imaging 2019: Image-Guided Procedures,...

2019
[80]

Self-supervised surgical tool segmentation using kinematic information

Cristian da Costa Rocha, Nicolas Padoy, and Benoit Rosa. Self-supervised surgical tool segmentation using kinematic information. In 2019 Interna- tional Conference on Robotics and Automation (ICRA), pages 8720–8726. IEEE, 2019

2019
[81]

Learning the representation of instrument images in laparoscopy videos

Sabrina Kletz, Klaus Schoeffmann, and Heinrich Husslein. Learning the representation of instrument images in laparoscopy videos. Healthcare Technology Letters, 6(6):197–203, 2019

2019
[82]

Ravasio, Lyndon Da Cruz, and Christos Bergeles

Theodoros Pissas, Claudio S. Ravasio, Lyndon Da Cruz, and Christos Bergeles. Effective semantic segmentation in cataract surgery: What matters most? CoRR, abs/2108.06119, 2021

2021 arXiv
[83]

Simulation- to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation

Manish Sahu, Anirban Mukhopadhyay, and Stefan Zachow. Simulation- to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation. International journal of computer assisted ra- diology and surgery, 16(5):849–859, 2021. Baoru Huang Gamma Probe fo...

2021
[84]

Surgical tool seg- mentation using generative adversarial networks with unpaired training data

Zhongkai Zhang, Beno ˆıt Rosa, and Florent Nageotte. Surgical tool seg- mentation using generative adversarial networks with unpaired training data. IEEE Robotics and Automation Letters, 6(4):6266–6273, 2021

2021
[85]

Surgical tool segmentation and localization using spatio-temporal deep network

Aparna Kanakatte, Akshaya Ramaswamy, Jayavardhana Gubbi, Avik Ghose, and Balamuralidhar Purushothaman. Surgical tool segmentation and localization using spatio-temporal deep network. In 2020 42nd an- nual international conference of the IEEE engineering in medicine & biology s...

2020
[86]

Colonoscopy 3d video dataset with paired depth from 2d-3d registration

Taylor L Bobrow, Mayank Golhar, Rohan Vijayan, Venkata S Akshintala, Juan R Garcia, and Nicholas J Durr. Colonoscopy 3d video dataset with paired depth from 2d-3d registration. Medical Image Analysis, page 102956, 2023

2023
[87]

Endomapper dataset of complete calibrated endoscopy procedures

Pablo Azagra, Carlos Sostres, ´Angel Ferrandez, Luis Riazuelo, Clara Tomasini, Oscar Le´on Barbed, Javier Morlana, David Recasens, Victor M Batlle, Juan J G´omez-Rodr´ıguez, et al. Endomapper dataset of complete calibrated endoscopy procedures. arXiv preprint arXiv:2204.14240, 2022

2022 arXiv
[88]

https:/ /github.com/arpg/vicalib

Vicalib. https:/ /github.com/arpg/vicalib. Accessed: 15-12-2023

2023
[89]

Endoslam dataset and an unsu- pervised monocular visual odometry and depth estimation approach for endoscopic videos

Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Taylor L Bobrow, Gulﬁze Coskun, Kagan Incetan, Yasin Almalioglu, Faisal Mahmood, Eva Curto, Luis Perdigoto, Marina Oliveira, et al. Endoslam dataset and an unsu- pervised monocular visual odometry and depth estimation approach for e...

2021
[90]

https:/ /www.mathworks.com/products/ computer-vision.html

Computer vision toolbox. https:/ /www.mathworks.com/products/ computer-vision.html. Accessed: 15-12-2023. Baoru Huang Gamma Probe for MIS 160 BIBLIOGRAPHY BIBLIOGRAPHY

2023
[91]

Implicit domain adapta- tion with conditional generative adversarial networks for depth prediction in endoscopy

Anita Rau, PJ Eddie Edwards, Omer F Ahmad, Paul Riordan, Mirek Janatka, Laurence B Lovat, and Danail Stoyanov. Implicit domain adapta- tion with conditional generative adversarial networks for depth prediction in endoscopy. International journal of computer assisted radiology ...

2019
[92]

Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery

Menglong Ye, Edward Johns, Ankur Handa, Lin Zhang, Philip Pratt, and Guang-Zhong Yang. Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery. arXiv preprint arXiv:1705.08260, 2017

2017 arXiv
[93]

Stereo correspondence and reconstruction of endoscopic data challenge

Max Allan, Jonathan Mcleod, Cong Cong Wang, Jean Claude Rosenthal, Ke Xue Fu, Trevor Zefﬁro, Wenyao Xia, Zhu Zhanshi, Huoling Luo, Xiran Zhang, et al. Stereo correspondence and reconstruction of endoscopic data challenge. arXiv:2101.01133, 2021

2021 arXiv
[94]

Deep monocular 3d reconstruction for assisted naviga- tion in bronchoscopy.International journal of computer assisted radiology and surgery, 12:1089–1099, 2017

Marco Visentini-Scarzanella, Takamasa Sugiura, Toshimitsu Kaneko, and Shinichiro Koto. Deep monocular 3d reconstruction for assisted naviga- tion in bronchoscopy.International journal of computer assisted radiology and surgery, 12:1089–1099, 2017

2017
[95]

Parallax attention for unsupervised stereo correspondence learning

Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, and Wei An. Parallax attention for unsupervised stereo correspondence learning. IEEE transactions on pattern analysis and ma- chine intelligence, 44(4):2108–2125, 2020

2020
[96]

Digging into self-supervised monocular depth estimation

Cl ´ement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Bros- tow. Digging into self-supervised monocular depth estimation. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision , pages 3828–3838, 2019. Baoru Huang Gamma Probe for MIS 161 BIBLIO...

2019
[97]

Reconstructing sinus anatomy from endoscopic video–towards a radiation-free approach for quantita- tive longitudinal assessment

Xingtong Liu, Maia Stiber, Jindan Huang, Masaru Ishii, Gregory D Hager, Russell H Taylor, and Mathias Unberath. Reconstructing sinus anatomy from endoscopic video–towards a radiation-free approach for quantita- tive longitudinal assessment. In Medical Image Computing and Compu...

2020
[98]

Adversarial do- main feature adaptation for bronchoscopic depth estimation

Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga, Wolfgang Wein, Nassir Navab, Federico Tombari, and Alexander Ladikos. Adversarial do- main feature adaptation for bronchoscopic depth estimation. In Medi- cal Image Computing and Computer Assisted Intervention–MICCAI 2021: ...

2021
[99]

Unsupervised monocular depth estimation for colonoscope system using feedback network

Seung-Jun Hwang, Sung-Jun Park, Gyu-Min Kim, and Joong-Hwan Baek. Unsupervised monocular depth estimation for colonoscope system using feedback network. Sensors, 21(8):2691, 2021

2021
[100]

On the uncertain single-view depths in colonoscopies

Javier Rodriguez-Puigvert, David Recasens, Javier Civera, and Ruben Martinez-Cantin. On the uncertain single-view depths in colonoscopies. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 130–140. Springer, 2022

2022
[101]

Geometric constraints for self-supervised monocular depth estimation on laparoscopic images with dual-task con- sistency

Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, and Kensaku Mori. Geometric constraints for self-supervised monocular depth estimation on laparoscopic images with dual-task con- sistency. In International Conference on Medical Image Computing and ...

2022
[102]

Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, and Kensaku Mori. Depth estimation from single-shot monocular endoscope image using image domain adapta- tion and edge-aware depth estimation.Computer Methods in Biomechan- ics and B...

2022
[103]

Geonet: Unsupervised learning of dense depth, optical ﬂow and camera pose

Zhichao Yin and Jianping Shi. Geonet: Unsupervised learning of dense depth, optical ﬂow and camera pose. In Proceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 1983–1992, 2018

1983
[104]

H-net: Unsupervised attention-based stereo depth estimation leveraging epipolar geometry

Baoru Huang, Jian-Qing Zheng, Stamatia Giannarou, and Daniel S Elson. H-net: Unsupervised attention-based stereo depth estimation leveraging epipolar geometry. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 4460–4467, 2022

2022
[105]

Details preserved unsuper- vised depth estimation by fusing traditional stereo knowledge from la- paroscopic images

Huoling Luo, Qingmao Hu, and Fucang Jia. Details preserved unsuper- vised depth estimation by fusing traditional stereo knowledge from la- paroscopic images. Healthcare technology letters, 6(6):154–158, 2019

2019
[106]

Unsupervised binocular depth pre- diction network for laparoscopic surgery

Ke Xu, Zhiyong Chen, and Fucang Jia. Unsupervised binocular depth pre- diction network for laparoscopic surgery. Computer Assisted Surgery , 24(sup1):30–35, 2019

2019
[107]

Endo-depth-and-motion: Reconstruction and tracking in endo- scopic videos using depth networks and photometric constraints

David Recasens, Jos ´e Lamarca, Jos ´e M F ´acil, JMM Montiel, and Javier Civera. Endo-depth-and-motion: Reconstruction and tracking in endo- scopic videos using depth networks and photometric constraints. IEEE Robotics and Automation Letters, 6(4):7225–7232, 2021

2021
[108]

Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D Hager, Austin Re- iter, Russell H Taylor, and Mathias Unberath. Dense depth estimation in Baoru Huang Gamma Probe for MIS 163 BIBLIOGRAPHY BIBLIOGRAPHY monocular endoscopy with self-supervised learning methods.IEEE trans- act...

2019
[109]

Dense depth estimation from stereo endoscopy videos using unsupervised op- tical ﬂow methods

Zixin Yang, Richard Simon, Yangming Li, and Cristian A Linte. Dense depth estimation from stereo endoscopy videos using unsupervised op- tical ﬂow methods. In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021,...

2021
[110]

Context-aware depth and pose estimation for bronchoscopic navigation

Mali Shen, Yun Gu, Ning Liu, and Guang-Zhong Yang. Context-aware depth and pose estimation for bronchoscopic navigation. IEEE Robotics and Automation Letters, 4(2):732–739, 2019

2019
[111]

Slam endoscopy enhanced by adversarial depth predic- tion

Richard J Chen, Taylor L Bobrow, Thomas Athey, Faisal Mahmood, and Nicholas J Durr. Slam endoscopy enhanced by adversarial depth predic- tion. arXiv preprint arXiv:1907.00283, 2019

1907 arXiv
[112]

Depth estimation for colonoscopy images with self-supervised learning from videos

Kai Cheng, Yiting Ma, Bin Sun, Yang Li, and Xuejin Chen. Depth estimation for colonoscopy images with self-supervised learning from videos. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 2...

2021
[113]

Foundationpose: Uniﬁed 6d pose estimation and tracking of novel objects

Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchﬁeld. Foundationpose: Uniﬁed 6d pose estimation and tracking of novel objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17868–17879, 2024

2024
[114]

Language-conditioned affordance- pose detection in 3d point clouds

Toan Nguyen, Minh Nhat Vu, Baoru Huang, Tuan Van Vo, Vy Truong, Ngan Le, Thieu Vo, Bac Le, and Anh Nguyen. Language-conditioned affordance- pose detection in 3d point clouds. In ICRA, 2024. Baoru Huang Gamma Probe for MIS 164 BIBLIOGRAPHY BIBLIOGRAPHY

2024
[115]

Stereo- scopic augmented reality for laparoscopic surgery

Xin Kang, Mahdi Azizian, Emmanuel Wilson, Kyle Wu, Aaron D Martin, Timothy D Kane, Craig A Peters, Kevin Cleary, and Raj Shekhar. Stereo- scopic augmented reality for laparoscopic surgery. Surgical endoscopy, 28(7):2227–2235, 2014

2014
[116]

Fused video and ultrasound images for minimally inva- sive partial nephrectomy: a phantom study

Carling L Cheung, Chris Wedlake, John Moore, Stephen E Pautler, and Terry M Peters. Fused video and ultrasound images for minimally inva- sive partial nephrectomy: a phantom study. In International Conference on Medical Image Computing and Computer-Assisted Intervention , page...

2010
[117]

Jayarathne, A

Uditha L. Jayarathne, A. Jonathan McLeod, Terry M. Peters, and Elvis C.S. Chen. Robust intraoperative US probe tracking using a monocular endo- scopic camera, 2013

2013
[118]

Calibration and stereo tracking of a laparoscopic ultrasound transducer for augmented reality in surgery, 2013

Philip Edgcumbe, Christopher Nguan, and Robert Rohling. Calibration and stereo tracking of a laparoscopic ultrasound transducer for augmented reality in surgery, 2013

2013
[119]

Robust, intrinsic tracking of a laparoscopic ultrasound probe for ultrasound-augmented laparoscopy

Uditha L Jayarathne, Elvis CS Chen, John Moore, and Terry M Pe- ters. Robust, intrinsic tracking of a laparoscopic ultrasound probe for ultrasound-augmented laparoscopy. IEEE transactions on medical imag- ing, 38(2):460–469, 2018

2018
[120]

Robust ultrasound probe tracking: initial clinical experiences during robot-assisted partial nephrectomy

Philip Pratt, Alexander Jaeger, Archie Hughes-Hallett, Erik Mayer, Justin Vale, Ara Darzi, Terry Peters, and Guang Zhong Yang. Robust ultrasound probe tracking: initial clinical experiences during robot-assisted partial nephrectomy. International Journal of Computer Assisted R...

1905
[121]

Real- time surgical tool tracking and pose estimation using a hybrid cylindri- Baoru Huang Gamma Probe for MIS 165 BIBLIOGRAPHY BIBLIOGRAPHY cal marker

Lin Zhang, Menglong Ye, Po Ling Chan, and Guang Zhong Yang. Real- time surgical tool tracking and pose estimation using a hybrid cylindri- Baoru Huang Gamma Probe for MIS 165 BIBLIOGRAPHY BIBLIOGRAPHY cal marker. International Journal of Computer Assisted Radiology and Surgery...

2017
[122]

Learning OpenCV: Computer vision with the OpenCV library

Gary Bradski and Adrian Kaehler. Learning OpenCV: Computer vision with the OpenCV library. ” O’Reilly Media, Inc.”, 2008

2008
[123]

ChESS - Quick and robust detection of chess-board features

Stuart Bennett and Joan Lasenby. ChESS - Quick and robust detection of chess-board features. Computer Vision and Image Understanding , 118:197–210, 2014

2014
[124]

Efﬁcient Non-Maximum Suppression

Alexander Neubeck. Efﬁcient Non-Maximum Suppression. pages 0–5, 2006

2006
[125]

Pyramidal implementation of the afﬁne lucas kanade feature tracker description of the algorithm

Jean-Yves Bouguet. Pyramidal implementation of the afﬁne lucas kanade feature tracker description of the algorithm. Intel Corporation, 5(1-10):4, 2001

2001
[126]

Inﬁnitesimal plane-based pose estima- tion

Toby Collins and Adrien Bartoli. Inﬁnitesimal plane-based pose estima- tion. International Journal of Computer Vision, 109(3):252–286, 2014

2014
[127]

A ﬂexible new technique for camera calibration

Zhengyou Zhang. A ﬂexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22, 2000

2000
[128]

Comparing two sets of corresponding six degree of freedom data

Mili Shah. Comparing two sets of corresponding six degree of freedom data. Computer Vision and Image Understanding , 115(10):1355–1362, 10 2011

2011
[129]

Minimally invasive and robotic surgery

Michael J Mack. Minimally invasive and robotic surgery. Jama, 285(5):568–572, 2001

2001
[130]

End-to-end real-time catheter segmenta- tion with optical ﬂow-guided warping during endovascular intervention

Anh Nguyen, Dennis Kundrat, Giulio Dagnino, Wenqiang Chi, Mo- hamed EMK Abdelaziz, Yao Guo, YingLiang Ma, Trevor MY Kwok, Celia Baoru Huang Gamma Probe for MIS 166 BIBLIOGRAPHY BIBLIOGRAPHY Riga, and Guang-Zhong Yang. End-to-end real-time catheter segmenta- tion with optical ﬂ...

2020
[131]

Evaluation and stability anal- ysis of video-based navigation system for functional endoscopic sinus surgery on in vivo clinical data

Simon Leonard, Ayushi Sinha, Austin Reiter, Masaru Ishii, Gary L Gallia, Russell H Taylor, and Gregory D Hager. Evaluation and stability anal- ysis of video-based navigation system for functional endoscopic sinus surgery on in vivo clinical data. IEEE transactions on medical i...

2018
[132]

Depth map prediction from a single image using a multi-scale deep network

David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. arXiv preprint arXiv:1406.2283, 2014

2014 arXiv
[133]

Multiple meta-model quantifying for medical visual question answering

Tuong Do, Binh X Nguyen, Erman Tjiputra, Minh Tran, Quang D Tran, and Anh Nguyen. Multiple meta-model quantifying for medical visual question answering. arXiv preprint arXiv:2105.08913, 2021

2021 arXiv
[134]

Efﬁcient deep learning for stereo matching

Wenjie Luo, Alexander G Schwing, and Raquel Urtasun. Efﬁcient deep learning for stereo matching. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5695–5703, 2016

2016
[135]

Pyramid stereo matching network

Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5410–5418, 2018

2018
[136]

Deeppruner: Learning efﬁcient stereo matching via differentiable patchmatch

Shivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, and Raquel Urta- sun. Deeppruner: Learning efﬁcient stereo matching via differentiable patchmatch. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4384–4393, 2019. Baoru Huang Gamma Probe for ...

2019
[137]

Unsupervised stereo matching using conﬁdential correspondence con- sistency

Sunghun Joung, Seungryong Kim, Kihong Park, and Kwanghoon Sohn. Unsupervised stereo matching using conﬁdential correspondence con- sistency. IEEE Transactions on Intelligent Transportation Systems , 21(5):2190–2203, 2019

2019
[138]

Unsupervised cnn for single view depth estimation: Geometry to the rescue

Ravi Garg, Vijay Kumar Bg, Gustavo Carneiro, and Ian Reid. Unsupervised cnn for single view depth estimation: Geometry to the rescue. InEuropean conference on computer vision, pages 740–756. Springer, 2016

2016
[139]

Unsu- pervised learning of depth and ego-motion from video

Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe. Unsu- pervised learning of depth and ego-motion from video. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1851–1858, 2017

2017
[140]

Unsupervised monocular depth estimation with left-right consistency

Cl ´ement Godard, Oisin Mac Aodha, and Gabriel J Brostow. Unsupervised monocular depth estimation with left-right consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 270–279, 2017

2017
[141]

Self-supervised monocular trained depth estimation using self-attention and discrete disparity vol- ume

Adrian Johnston and Gustavo Carneiro. Self-supervised monocular trained depth estimation using self-attention and discrete disparity vol- ume. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4756–4765, 2020

2020
[142]

Spatial transformer networks

Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Ko- ray Kavukcuoglu. Spatial transformer networks. arXiv preprint arXiv:1506.02025, 2015

2015 arXiv
[143]

Unsuper- vised adversarial depth estimation using cycled generative networks

Andrea Pilzer, Dan Xu, Mihai Puscas, Elisa Ricci, and Nicu Sebe. Unsuper- vised adversarial depth estimation using cycled generative networks. In 2018 International Conference on 3D Vision (3DV) , pages 587–595. IEEE, 2018. Baoru Huang Gamma Probe for MIS 168 BIBLIOGRAPHY BIBLIOGRAPHY

2018
[144]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

2016
[145]

Fast and accurate deep network learning by exponential linear units (elus)

Djork-Arn ´e Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289, 2015

2015 arXiv
[146]

Genera- tive adversarial networks

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Genera- tive adversarial networks. arXiv preprint arXiv:1406.2661, 2014

2014 arXiv
[147]

Dualgan: Unsupervised dual learning for image-to-image translation

Zili Yi, Hao Zhang, Ping Tan, and Minglun Gong. Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision, pages 2849–2857, 2017

2017
[148]

Unpaired image-to-image translation using cycle-consistent adversarial networks

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision , pages 2223–2232, 2017

2017
[149]

Im- age quality assessment: from error visibility to structural similarity

Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Im- age quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004

2004
[150]

Pm- huber: Patchmatch with huber regularization for stereo matching

Philipp Heise, Sebastian Klose, Brian Jensen, and Alois Knoll. Pm- huber: Patchmatch with huber regularization for stereo matching. InPro- ceedings of the IEEE International Conference on Computer Vision , pages 2360–2367, 2013

2013
[151]

Self-supervised monocular depth hints

Jamie Watson, Michael Firman, Gabriel J Brostow, and Daniyar Tur- mukhambetov. Self-supervised monocular depth hints. InProceedings of Baoru Huang Gamma Probe for MIS 169 BIBLIOGRAPHY BIBLIOGRAPHY the IEEE/CVF International Conference on Computer Vision , pages 2162– 2171, 2019

2019
[152]

Efﬁcient large-scale stereo matching

Andreas Geiger, Martin Roser, and Raquel Urtasun. Efﬁcient large-scale stereo matching. In Asian conference on computer vision , pages 25–38. Springer, 2010

2010
[153]

Efﬁcient joint segmentation, occlusion labeling, stereo and ﬂow estimation

Koichiro Yamaguchi, David McAllester, and Raquel Urtasun. Efﬁcient joint segmentation, occlusion labeling, stereo and ﬂow estimation. In Euro- pean Conference on Computer Vision, pages 756–771. Springer, 2014

2014
[154]

Automatic differentiation in pytorch

Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017

2017
[155]

Light-weight deformable registration using adversarial learning with distilling knowledge

Minh Q Tran, Tuong Do, Huy Tran, Erman Tjiputra, Quang D Tran, and Anh Nguyen. Light-weight deformable registration using adversarial learning with distilling knowledge. IEEE Transactions on Medical Imaging, 2022

2022
[156]

Vision meets robotics: The kitti dataset

Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231–1237, 2013

2013
[157]

Language-driven scene synthesis using multi- conditional diffusion model

An Dinh Vuong, Minh Nhat Vu, Toan Nguyen, Baoru Huang, Dzung Nguyen, Thieu Vo, and Anh Nguyen. Language-driven scene synthesis using multi- conditional diffusion model. InAdvances in Neural Information Processing Systems, volume 36, 2024

2024
[158]

3d packing for self-supervised monocular depth estimation

Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2485–2494, 2020. Baoru Huang Gamma Probe for ...

2020
[159]

Self-supervised monocular depth estimation: Solving the dy- namic object problem by semantic guidance

Marvin Klingner, Jan-Aike Term ¨ohlen, Jonas Mikolajczyk, and Tim Fin- gscheidt. Self-supervised monocular depth estimation: Solving the dy- namic object problem by semantic guidance. In European Conference on Computer Vision, pages 582–600. Springer, 2020

2020
[160]

Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation

Hyunyoung Jung, Eunhyeok Park, and Sungjoo Yoo. Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 12642–12652, 2021

2021
[161]

Unsupervised learning of depth and ego-motion from monocular video using 3d geo- metric constraints

Reza Mahjourian, Martin Wicke, and Anelia Angelova. Unsupervised learning of depth and ego-motion from monocular video using 3d geo- metric constraints. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5667–5675, 2018

2018
[162]

Rectiﬁed linear units improve restricted boltzmann machines

Vinod Nair and Geoffrey E Hinton. Rectiﬁed linear units improve restricted boltzmann machines. In Icml, 2010

2010
[163]

Raft-stereo: Multilevel recur- rent ﬁeld transforms for stereo matching

Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recur- rent ﬁeld transforms for stereo matching. In 2021 International Confer- ence on 3D Vision (3DV), pages 218–227. IEEE, 2021

2021
[164]

Efﬁcient variants of the icp algo- rithm

Szymon Rusinkiewicz and Marc Levoy. Efﬁcient variants of the icp algo- rithm. In Proceedings third international conference on 3-D digital imaging and modeling, pages 145–152. IEEE, 2001

2001
[165]

Robust pixel classiﬁcation for 3d modeling with structured light

Yi Xu and Daniel G Aliaga. Robust pixel classiﬁcation for 3d modeling with structured light. In Proceedings of Graphics Interface 2007 , pages 233–240, 2007

2007
[166]

A self-adaptive motion scaling framework for surgical robot Baoru Huang Gamma Probe for MIS 171 BIBLIOGRAPHY BIBLIOGRAPHY remote control

Dandan Zhang, Bo Xiao, Baoru Huang, Lin Zhang, Jindong Liu, and Guang- Zhong Yang. A self-adaptive motion scaling framework for surgical robot Baoru Huang Gamma Probe for MIS 171 BIBLIOGRAPHY BIBLIOGRAPHY remote control. IEEE Robotics and Automation Letters , 4(2):359–366, 2018

2018
[167]

Vision-based deformation re- covery for intraoperative force estimation of tool–tissue interaction for neurosurgery

Stamatia Giannarou, Menglong Ye, Gauthier Gras, Konrad Leibrandt, Hani J Marcus, and Guang-Zhong Yang. Vision-based deformation re- covery for intraoperative force estimation of tool–tissue interaction for neurosurgery. International journal of computer assisted radiology and ...

2016
[168]

Long-tailed instance segmentation using gumbel optimized loss

Konstantinos Panagiotis Alexandridis, Jiankang Deng, Anh Nguyen, and Shan Luo. Long-tailed instance segmentation using gumbel optimized loss. In European Conference on Computer Vision , pages 353–369. Springer, 2022

2022
[169]

Three ways to improve semantic segmentation with self- supervised depth estimation

Lukas Hoyer, Dengxin Dai, Yuhua Chen, Adrian Koring, Suman Saha, and Luc Van Gool. Three ways to improve semantic segmentation with self- supervised depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11130–11140, 2021

2021
[170]

Unsupervised surgical instrument segmentation via anchor generation and semantic diffusion

Daochang Liu, Yuhui Wei, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, and Ziyu Li. Unsupervised surgical instrument segmentation via anchor generation and semantic diffusion. In Medical Image Comput- ing and Computer Assisted Intervention–MICCAI 2020: 23rd International ...

2020
[171]

Autonomous tissue scanning under free-form motion for intraoperative tissue characterisa- tion

Jian Zhan, Joao Cartucho, and Stamatia Giannarou. Autonomous tissue scanning under free-form motion for intraoperative tissue characterisa- tion. In 2020 IEEE international conference on robotics and automation (ICRA), pages 11147–11154. IEEE, 2020. Baoru Huang Gamma Probe for...

2020
[172]

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

Nicolas Zevallos, Rangaprasad Arun Srivatsan, Hadi Salman, Lu Li, Jian- ing Qian, Saumya Saxena, Mengyun Xu, Kartik Patath, and Howie Choset. A surgical system for automatic registration, stiffness mapping and dynamic image overlay. In 2018 International Symposium on Medical R...

2018
[173]

Real-time joint semantic segmentation and depth estimation using asymmetric annotations

Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek, Tom Drummond, Chunhua Shen, and Ian Reid. Real-time joint semantic segmentation and depth estimation using asymmetric annotations. In 2019 International Conference on Robotics and Automation (ICRA) , pages 7101–7107. IEEE, 2019

2019
[174]

Revisiting sin- gle image depth estimation: Toward higher resolution maps with accu- rate object boundaries

Junjie Hu, Mete Ozay, Yan Zhang, and Takayuki Okatani. Revisiting sin- gle image depth estimation: Toward higher resolution maps with accu- rate object boundaries. In 2019 IEEE winter conference on applications of computer vision (WACV), pages 1043–1051. IEEE, 2019

2019
[175]

Sfm-net: Learning of structure and motion from video

Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, and Katerina Fragkiadaki. Sfm-net: Learning of structure and motion from video. arXiv preprint arXiv:1704.07804, 2017

2017 arXiv
[176]

Tracking of instruments in minimally invasive surgery for surgical skill analysis

Stefanie Speidel, Michael Delles, Carsten Gutt, and R ¨udiger Dillmann. Tracking of instruments in minimally invasive surgery for surgical skill analysis. In Medical Imaging and Augmented Reality: Third International Workshop, Shanghai, China, August 17-18, 2006 Proceedings 3 ...

2006
[177]

Articulated object tracking by rendering consistent appearance parts

Zachary Pezzementi, Sandrine Voros, and Gregory D Hager. Articulated object tracking by rendering consistent appearance parts. In 2009 IEEE International Conference on Robotics and Automation, pages 3940–3947. IEEE, 2009. Baoru Huang Gamma Probe for MIS 173 BIBLIOGRAPHY BIBLIOGRAPHY

2009
[178]

Real-time instrument segmentation in robotic surgery using auxiliary supervised deep adversarial learning

Mobarakol Islam, Daniel Anojan Atputharuban, Ravikiran Ramesh, and Hongliang Ren. Real-time instrument segmentation in robotic surgery using auxiliary supervised deep adversarial learning. IEEE Robotics and Automation Letters, 4(2):2188–2195, 2019

2019
[179]

Edgestereo: A context integrated residual pyramid network for stereo matching

Xiao Song, Xu Zhao, Hanwen Hu, and Liangji Fang. Edgestereo: A context integrated residual pyramid network for stereo matching. In Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Aus- tralia, December 2–6, 2018, Revised Selected Papers, Part V 14, pages 20–

2018
[180]

Self-supervised depth estimation to regularise seman- tic segmentation in knee arthroscopy

Fengbei Liu, Yaqub Jonmohamadi, Gabriel Maicas, Ajay K Pandey, and Gustavo Carneiro. Self-supervised depth estimation to regularise seman- tic segmentation in knee arthroscopy. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Confer...

2020
[181]

Loss functions for neural networks for image processing

Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz. Loss functions for neural networks for image processing. arXiv preprint arXiv:1511.08861 , 2015

2015 arXiv
[182]

Fully convolutional networks for semantic segmentation

Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 3431–3440, 2015

2015
[183]

Three- dimensional tissue deformation recovery and tracking

Peter Mountney, Danail Stoyanov, and Guang-Zhong Yang. Three- dimensional tissue deformation recovery and tracking. IEEE Signal Pro- cessing Magazine, 27(4):14–24, 2010

2010
[184]

Learning motion ﬂows for semi-supervised instrument segmentation Baoru Huang Gamma Probe for MIS 174 BIBLIOGRAPHY BIBLIOGRAPHY from robotic surgical video

Zixu Zhao, Yueming Jin, Xiaojie Gao, Qi Dou, and Pheng-Ann Heng. Learning motion ﬂows for semi-supervised instrument segmentation Baoru Huang Gamma Probe for MIS 174 BIBLIOGRAPHY BIBLIOGRAPHY from robotic surgical video. In Medical Image Computing and Com- puter Assisted Inter...

2020
[185]

Adam: A method for stochastic opti- mization

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic opti- mization. arXiv preprint arXiv:1412.6980, 2014

2014 arXiv
[186]

Robust real-time detection of laparoscopic instruments in robot surgery using convolutional neural networks with motion vector prediction.Applied Sci- ences, 9(14):2865, 2019

Kyungmin Jo, Yuna Choi, Jaesoon Choi, and Jong Woo Chung. Robust real-time detection of laparoscopic instruments in robot surgery using convolutional neural networks with motion vector prediction.Applied Sci- ences, 9(14):2865, 2019

2019
[187]

Surgical scene segmentation using semantic image synthesis with a virtual surgery environment

Jihun Yoon, SeulGi Hong, Seungbum Hong, Jiwon Lee, Soyeon Shin, Bokyung Park, Nakjun Sung, Hayeong Yu, Sungjae Kim, SungHyun Park, et al. Surgical scene segmentation using semantic image synthesis with a virtual surgery environment. In Medical Image Computing and Com- puter As...

2022
[188]

Stereo depth estimation via self-supervised contrastive representation learning

Samyakh Tukra and Stamatia Giannarou. Stereo depth estimation via self-supervised contrastive representation learning. In International Con- ference on Medical Image Computing and Computer-Assisted Intervention, pages 604–614. Springer, 2022

2022
[189]

Sage: Slam with appearance and geometry prior for endoscopy

Xingtong Liu, Zhaoshuo Li, Masaru Ishii, Gregory D Hager, Russell H Tay- lor, and Mathias Unberath. Sage: Slam with appearance and geometry prior for endoscopy. In 2022 International Conference on Robotics and Automation (ICRA), pages 5587–5593. IEEE, 2022. Baoru Huang Gamma P...

2022
[190]

Svt-sde: Spa- tiotemporal vision transformers-based self-supervised depth estimation in stereoscopic surgical videos

Rong Tao, Baoru Huang, Xiaoyang Zou, and Guoyan Zheng. Svt-sde: Spa- tiotemporal vision transformers-based self-supervised depth estimation in stereoscopic surgical videos. IEEE Transactions on Medical Robotics and Bionics, 2023

2023
[191]

A multi-task convolutional neural network for semantic segmentation and event detection in laparoscopic surgery

Giorgia Marullo, Leonardo Tanzi, Luca Ulrich, Francesco Porpiglia, and Enrico Vezzetti. A multi-task convolutional neural network for semantic segmentation and event detection in laparoscopic surgery. Journal of Personalized Medicine, 13(3):413, 2023

2023
[192]

Ds-transunet: Dual swin transformer u-net for medical im- age segmentation

Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guangming Lu, and David Zhang. Ds-transunet: Dual swin transformer u-net for medical im- age segmentation. IEEE Transactions on Instrumentation and Measure- ment, 71:1–15, 2022

2022
[193]

Swin-unet: Unet-like pure transformer for medical image segmentation

Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In Computer Vision–ECCV 2022 Work- shops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III , pages 205–2...

2022
[194]

Transunet: Transformers make strong encoders for medical image segmentation

Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306, 2021

2021 arXiv
[195]

Principal components analysis (pca)

Andrzej Ma ´ckiewicz and Waldemar Ratajczak. Principal components analysis (pca). Computers & Geosciences, 19(3):303–342, 1993

1993
[196]

An image is worth 16x16 words: Trans- formers for image recognition at scale

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Baoru Huang Gamma Probe for MIS 176 BIBLIOGRAPHY BIBLIOGRAPHY Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Tra...

2010 arXiv
[197]

Long short-term memory

Sepp Hochreiter and J ¨urgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997

1997
[198]

Language-driven grasp detection

An Dinh Vuong, Minh Nhat Vu, Baoru Huang, Nghia Nguyen, Hieu Le, Thieu Vo, and Anh Nguyen. Language-driven grasp detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17902–17912, 2024

2024
[199]

Grasp-anything: Large-scale grasp dataset from foundation models

An Dinh Vuong, Minh Nhat Vu, Hieu Le, Baoru Huang, Binh Huynh, Thieu Vo, Andreas Kugi, and Anh Nguyen. Grasp-anything: Large-scale grasp dataset from foundation models. In ICRA, 2024. Baoru Huang Gamma Probe for MIS 177

2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Robinson

Robert D. Robinson. Reliable Machine Learning for Medical Imaging Data through Automated Quality Control and Data Harmonization. 7 2020

2020

[2] [2]

Detecting the sensing area of a laparoscopic probe in mini- mally invasive cancer surgery

Baoru Huang, Yicheng Hu, Anh Nguyen, Stamatia Giannarou, and Daniel S Elson. Detecting the sensing area of a laparoscopic probe in mini- mally invasive cancer surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention , pages 260–270. Springer, 2023

2023

[3] [3]

Self- supervised depth estimation in laparoscopic image using 3d geometric consistency

Baoru Huang, Jian-Qing Zheng, Anh Nguyen, Chi Xu, Ioannis Gkouzionis, Kunal Vyas, David Tuch, Stamatia Giannarou, and Daniel S Elson. Self- supervised depth estimation in laparoscopic image using 3d geometric consistency. In Medical Image Computing and Computer Assisted Inter- vention, 2022

2022

[4] [4]

Self-supervised generative adver- sarial network for depth estimation in laparoscopic images

Baoru Huang, Jian-Qing Zheng, Anh Nguyen, David Tuch, Kunal Vyas, Sta- matia Giannarou, and Daniel S Elson. Self-supervised generative adver- sarial network for depth estimation in laparoscopic images. In Medi- cal Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 202...

2021

[5] [5]

Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe

Baoru Huang, Ya-Yen Tsai, Jo ˜ao Cartucho, Kunal Vyas, David Tuch, Sta- matia Giannarou, and Daniel S Elson. Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe. International 146 BIBLIOGRAPHY BIBLIOGRAPHY Journal of Computer Assisted Radiology and Surgery , 15(8):1389–1397, 2020

2020

[6] [6]

Simultaneous depth estimation and surgical tool segmentation in laparoscopic images

Baoru Huang, Anh Nguyen, Siyao Wang, Ziyang Wang, Erik Mayer, David Tuch, Kunal Vyas, Stamatia Giannarou, and Daniel S Elson. Simultaneous depth estimation and surgical tool segmentation in laparoscopic images. IEEE Transactions on Medical Robotics and Bionics, 4(2):335–338, 2022

2022

[7] [7]

Residual aligner-based network (ran): Motion- separable structure for coarse-to-ﬁne discontinuous deformable registra- tion

Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, and Bart lomiej W Papie ˙z. Residual aligner-based network (ran): Motion- separable structure for coarse-to-ﬁne discontinuous deformable registra- tion. Medical Image Analysis, 91:103038, 2024

2024

[8] [8]

Deep imitation learning for automated drop-in gamma probe manipulation

Kaizhong Deng, Baoru Huang, and Daniel S Elson. Deep imitation learning for automated drop-in gamma probe manipulation. Hamlyn Symposium 2023, 2023

2023