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REVIEW 4 major objections 6 minor 1 cited by

SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read SplineFormer predicts a guidewire's B-spline shape from X-ray images and uses it to drive a robot that cannulates the brachiocephalic artery autonomously 50% of the time.

desk verdict The spline-shape prediction is a genuinely useful idea, but the autonomous navigation claim is not yet supported because the policy head has no described training objective. read the letter →

arxiv 2501.04515 v1 pith:EIMKF3DJ submitted 2025-01-08 eess.IV cs.CVcs.RO

Tudor Jianu , Shayan Doust , Mengyun Li , Baoru Huang , Tuong Do , Hoan Nguyen , Karl Bates , Tung D. Ta
show 3 more authors
Sebastiano Fichera Pierre Berthet-Rayne Anh Nguyen
This is my paper · ORCID
classification eess.IVcs.CVcs.RO
keywords EndovascularnavigationGuidewireshapepredictionB-splinerepresentationTransformerAutonomousExplainableAIFluoroscopyRobot-assistedintervention
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

SplineFormer claims that a transformer can infer a guidewire's continuous shape directly from fluoroscopic X-ray images, representing it as a B-spline curve rather than a segmentation mask. This condensed geometric representation is then used as the state for an end-to-end robot control pipeline, and the paper reports that the resulting system cannulates the brachiocephalic artery in a physical vascular phantom fully autonomously in 50% of 20 trials. The authors argue this beats a fully autonomous behavior-cloning baseline (5.6% success) and offers an explainable, fragmentation-free alternative to segmentation-based methods, at the cost of lower success than a semi-autonomous GAIL-PPO approach. If true, the result suggests that direct spline-parameter prediction is a viable state representation for autonomous endovascular navigation.

What carries the argument

The central machinery is the B-spline curve model $C(t) = \sum_{i=0}^{n} P_i B_{i,p}(t)$, which encodes the guidewire as a small set of control points $P_i$ and knot values $t_i$, giving a smooth, continuous curve with local control. A visual-transformer encoder processes X-ray image patches; a transformer decoder with masked self-attention generates the coefficient–knot pairs autoregressively, starting from a separately predicted tip point; a curvature-consistency loss samples the predicted curve to keep it smooth; and a PolicyConv head maps the decoder's condensed state to translation/rotation commands for the robot.

What would settle it

Inspect the training code and learning curves for the PolicyConv action head: if no loss or gradient ever updates that head (no behavior-cloning term, no RL return, no auxiliary action loss), then the reported 50% robot success rate cannot be produced by the described method.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a transformer-based network can predict the B-spline coefficients and knot sequence that define a guidewire's shape in fluoroscopy, and that this spline, together with a learned action head, carries enough information for a physical robot to navigate a guidewire to a target vessel without human intervention. The network is trained with a composite loss on spline-parameter prediction, end-of-sequence classification, and sampled-curve curvature consistency; the robot's translation and rotation actions come from a PolicyConv head attached to the decoder output. In evaluation, SplineFormer reaches the brachiocephalic artery in 50% of 20 fully autonomous trials, with a mean completion time of 2.5 minutes, and cannot reach the left common carotid artery, a limitation the authors attribute to the more complex 3D anatomy of that branch.

Load-bearing premise

The autonomous navigation claim rests on the unstated assumption that the PolicyConv action head is trained by a well-defined objective; the only loss the paper reports covers spline parameters, end-of-sequence, and curvature, with no action term, so the 50% success rate is not derivable from the described training procedure.

Editorial extensions

If this is right

  • If SplineFormer is correct, segmentation-free guidewire shape prediction can feed a robot controller directly, avoiding the discontinuous masks that plague U-Net-style segmentation.
  • Fully autonomous cannulation of the brachiocephalic artery at 50% success on a real robot, against a 5.6% behavior-cloning baseline, indicates the spline state carries enough information for navigation decisions.
  • The method's explainability, with attention concentrated near the guidewire tip and target branches, could make robot decisions easier for clinicians to verify during procedures.
  • The inability to cannulate the left common carotid artery shows the approach needs stronger handling of complex 3D anatomy before clinical translation.
  • Because the spline is a compact parameterization, the approach may scale to real-time use with modest compute, as demonstrated on an RTX 4080 GPU.

Reading between the lines

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

  • The reported comparison is uneven: SplineFormer is fully autonomous while GAIL-PPO receives teleoperator assistance; a fair test of the representation would pit the spline state against a mask-based state inside the same fully autonomous policy.
  • The absence of a policy-loss term in the write-up makes it likely that the PolicyConv action head is trained by some implicit gradient from the composite loss or by a procedure the paper omits; pinning this down is the fastest way to test the navigation claim.
  • The same B-spline regression idea could transfer to other thin deformable instruments, such as catheters, ablation catheters, or endoscopes, and to other imaging modalities, since the representation is modality-agnostic once the tool is visible.
  • A testable extension would be to run SplineFormer with a frozen spline predictor and a randomly initialized action head; if the 50% success persists, the navigation result is not attributable to shape prediction.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes SplineFormer, a transformer-based architecture that predicts B-spline control points and knots representing the guidewire directly from X-ray images, thereby bypassing pixel-wise segmentation. The predicted spline is fed into a PolicyConv head that outputs translation and rotation commands for a physical endovascular robot. Experiments on a vascular phantom with a leader-follower robot report a 50% success rate in cannulating the brachiocephalic artery, compared with 5.6% for a behavior-cloning baseline, and the paper also presents qualitative shape-prediction results and attention visualizations.

Significance. If the experimental claims are reproducible, the paper would make a useful contribution: direct spline prediction is a plausible way to avoid the fragmentation problems of segmentation, and a fully autonomous 50% cannulation rate on a physical phantom is a nontrivial result. Strengths include the use of real bi-planar X-ray data (8,746 annotated images), a physical robot evaluation, and a comparison with an existing autonomous baseline. However, the claims are currently contingent on a missing description of the policy training objective, the absence of quantitative shape-prediction metrics, and weak statistical reporting. I do not see a circularity problem, because the evaluation uses external baselines on a real robot; the spline representation is not reducing to fitted constants in a circular way. The 'explainable' claim rests only on attention visualizations and has no quantitative support.

major comments (4)
  1. [Section IV.C, Eq. (8); Fig. 4] The autonomous navigation claim rests on the PolicyConv action head, but no training objective for this head is specified. The global loss in Eq. (8) contains only the spline-parameter MSE, the end-of-sequence cross-entropy, and the curvature-consistency term; there is no action loss, behavior-cloning loss, or reinforcement-learning objective. Section V.A states that the learned policy is evaluated on state-action pairs and that SplineFormer is deployed after training, but the training procedure that maps spline or image features to translation and rotation commands is never described. Without this component, the reported 50% success rate cannot be reproduced or even evaluated from the manuscript. The authors must state how PolicyConv is trained, including the objective, the data used, and any action-space preprocessing.
  2. [Section V.A, Table I] The headline result is reported as a 50% success rate over 20 trials, but the manuscript does not define what constitutes a successful cannulation, does not report the number of successes (10/20 is only inferred), and gives no confidence interval or statistical comparison. It is also unclear whether unsuccessful trials are included in the mean completion time of 150 ± 45.6 s. The comparison to Behavior Cloning (5.6%) and GAIL-PPO (69.4% semi-autonomous) has no significance testing and mixes fully autonomous with semi-autonomous conditions. Finally, Table I leaves the LCCA entry for SplineFormer empty even though the text states that the method could not cannulate the LCCA; if that target was attempted, the success rate and trial count should be reported. Please define the success metric, report per-target trial counts, and provide confidence intervals or raw outcomes.
  3. [Section V.B, Fig. 7] The shape-prediction component, which is the core of the proposed representation, is evaluated only qualitatively. No quantitative error metric (e.g., endpoint error, control-point error, or curve distance) is reported on a held-out test set, and there is no ablation for the loss weights or the number of control points. Because the autonomous navigation system depends on the accuracy of the predicted spline, the paper needs a quantitative evaluation of shape prediction and an analysis of how prediction errors affect navigation outcomes.
  4. [Section IV.B, IV.C] Several architecture and training hyperparameters required for reproduction are missing. The patch size |P|, the number of encoder and decoder layers N_e and N_D, the number of heads H, the loss weights λ_a, λ_b, λ_c, the curvature sample count n, and the number of control points in the output sequence are not given; Eq. (8) also uses N without defining it. Including these values, along with the learning-rate schedule and any data augmentation, is necessary for the experimental claims to be reproducible.
minor comments (6)
  1. [Section III] The driver part number 'A4899' appears to be a typo for 'A4988'; please correct it.
  2. [Section IV.A, Eq. (3)] The B-spline recursion is undefined when knot values coincide; the standard 0/0 = 0 convention should be stated explicitly.
  3. [Section V.A] The action space is described as a_t ∈ [−1, 1]^2 corresponding to a maximum translation of 2 mm and a rotation of 15°, but the text does not explain how the continuous PolicyConv output is mapped to these limits or whether the actions are relative or absolute.
  4. [Section V.C] The 'discard factor' used in the attention-map fusion is not defined, which makes the attention visualization procedure non-reproducible.
  5. [Section II] Citation [43] is cited for 'advancements in autonomous driving,' but [43] is a guidewire tracking paper; this citation appears mismatched.
  6. [Sections I and II] There are several grammatical errors, including 'most studies were conducted' (should be 'most studies being conducted' or similar) and 'deep learning models face challenges often struggle' (should be 'deep learning models often struggle').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the shape-prediction and navigation claims rest on supervised training and empirical robot trials, not on definitions or fitted constants.

full rationale

SplineFormer's central derivation chain is empirical. The spline geometry output is trained with the supervised loss in Eq. (8) against manually annotated B-spline targets derived from polylines, and the shape-prediction quality is evaluated against U-Net on X-ray images. The autonomous-navigation result (50% BCA success) is an experimental outcome measured on a physical robot over 20 trials and compared with an external Behavior Cloning baseline [22]; it is not produced by the loss equation or by the spline representation itself. The B-spline premise is imported from an external citation [56], not from the authors' own prior work. Self-citations present ([33], [45], [46], [48]) appear only in related-work or method-context sentences and are not used as the justification for any derived claim; no uniqueness theorem or ansatz is imported from the authors' prior papers. The absence of an explicit training objective for the PolicyConv action head (Eq. (8) contains only spline MSE, end-of-sequence BCE, and curvature consistency) is a completeness gap in the method description, but it is an omitted component rather than a statement whose conclusion is equivalent to its premise by construction. Under the rule that circularity requires a quotable reduction of a result to its own inputs, no such step is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central navigation claim relies on an unspecified policy training objective and several hand-chosen hyperparameters, while the shape representation is an existing mathematical construct; no new entities are introduced.

free parameters (3)
  • Loss weights lambda_a, lambda_b, lambda_c = not reported
    Hand-chosen balance between shape MSE, end-of-sequence BCE, and curvature consistency; no values or tuning details are given, making the training recipe incomplete.
  • Number of control points N (sequence length) = not reported
    Determines the expressiveness of the spline and the output sequence length; no ablation is provided.
  • Vision transformer patch size |P| = not reported
    Input patch size is a hyperparameter that affects performance and is not specified in the text.
assumptions (4)
  • domain assumption A guidewire in fluoroscopy can be accurately represented as a B-spline with a small number of control points and knots.
    The entire representation rests on this claim (citing [56]); if wrong, the predicted geometry is biased.
  • domain assumption The X-ray image contains sufficient information to predict the guidewire's full geometry.
    The network is trained end-to-end from image patches to spline parameters; if the wire is occluded or low-contrast, this fails.
  • domain assumption The vascular phantom and the two guidewires used in data collection are representative of the clinical anatomy and tool behavior for navigation.
    The navigation experiments are in a phantom, and the paper implies transfer to human anatomy without a validation step.
  • ad hoc to paper An effective action policy can be derived from the learned spline representation without an explicit policy loss.
    The paper does not describe a loss for the PolicyConv action head, yet claims autonomous navigation; this is an unsupported assumption in the current write-up.

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Cite this review

Pith. "Pith review of SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation." pith.science (2026). https://pith.science/paper/EIMKF3DJ

@misc{pith2026250104515,
  author       = {Pith},
  title        = {Pith review of: SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EIMKF3DJ}},
  note         = {Machine review of arXiv:2501.04515}
}
read the original abstract

Endovascular navigation is a crucial aspect of minimally invasive procedures, where precise control of curvilinear instruments like guidewires is critical for successful interventions. A key challenge in this task is accurately predicting the evolving shape of the guidewire as it navigates through the vasculature, which presents complex deformations due to interactions with the vessel walls. Traditional segmentation methods often fail to provide accurate real-time shape predictions, limiting their effectiveness in highly dynamic environments. To address this, we propose SplineFormer, a new transformer-based architecture, designed specifically to predict the continuous, smooth shape of the guidewire in an explainable way. By leveraging the transformer's ability, our network effectively captures the intricate bending and twisting of the guidewire, representing it as a spline for greater accuracy and smoothness. We integrate our SplineFormer into an end-to-end robot navigation system by leveraging the condensed information. The experimental results demonstrate that our SplineFormer is able to perform endovascular navigation autonomously and achieves a 50% success rate when cannulating the brachiocephalic artery on the real robot.

Figures

Figures reproduced from arXiv: 2501.04515 by the authors.

Figure 1
Figure 1. System Overview: The experimental setup includes three main components: i) an anatomically accurate half￾body vascular phantom model from Elastrat Sarl Ltd.; ii) a joystick controller (robotic leader); and iii) a robotic follower. Data is collected through teleoperation, where the robotic follower is controlled by the joystick controller. To achieve the precise navigation required for endovas￾cular interventions, op… view at source ↗
Figure 2
Figure 2. Segmentation Failure Cases. Due to challenges in capturing thin and elongated guidewire, segmentation models often produce discontinuous segmented maps. Autonomous Navigation. The navigation of surgical tools within the vasculature represents a strenuous task that has seen rapid advancement due to its promising benefits (i.e. reduced operative times and diminished radiation exposure). In this area of research, works… view at source ↗
Figure 3
Figure 3. Robot Setup in X-ray Room: The data acquisition and teleoperation process is highlighted, showcasing the user navigating the guidewire towards the designated target within the vascular phantom. The setup employs a Bi-planar X-ray. To enhance data variability, two different guidewires are used—the Radifocus™ Guide Wire M Stiff Type with an angled tip and the Nitrex Guidewire straight tip. grids. To simulate human vas… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Network Architecture: The input fluoroscopic image X is processed by a visual transformer encoder that divides the image into patches, embeds them, and generates visual feature representations X′ . Simultaneously, a positional encoder processes embeddings of the target…
Figure 5
Figure 5. Figure 5: B-spline Representation of a Guidewire: The figure illustrates how a guidewire, as seen in fluoroscopic imaging, can be represented as a continuous B-spline curve. B. Network Architecture Encoder. Two encoders are used, with the first encoder resembling a visual transf…
Figure 6
Figure 6. Figure 6: Navigation setup. positional encodings to capture the order of the sequence. The masked self-attention mechanism ensures autoregressive generation by restricting attention to previous or current coef￾ficients, enforcing a sequential prediction. The final decoder output…
Figure 8
Figure 8. Figure 8: Attention Visualization: Attention maps generated through maximal fusion of attention heads [57] highlight key regions where the guidewire tip is located, such as the central portion of the aortic arch, the ascending aorta, and the BCA cannulation target. traditional s…
Figure 7
Figure 7. Figure 7: Comparison between SplineFormer and segmen￾tation method: SplineFormer (left column) directly predicts the guidewire’s geometry using a condensed information space. In contrast, U-Net generates segmentation masks with￾out providing detailed geometric understanding, req…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions

    cs.RO 2025-06 reject novelty 4.0 of 10

    A CTA-guided inverse projection framework for real-time 3D guidewire reconstruction from single-view DSA is proposed, but the depth formula and evaluation do not establish true 3D accuracy.

Reference graph

Works this paper leans on

61 extracted references · 55 canonical work pages · cited by 1 Pith paper

  1. [22]

    Collaborative robot-assisted endovascular catheterization with generative adversarial imitation learning,

    W. Chi, G. Dagnino, T. M. Kwok, A. Nguyen, D. Kundrat, M. E. Abdelaziz, C. Riga, C. Bicknell, and G.-Z. Yang, “Collaborative robot-assisted endovascular catheterization with generative adversarial imitation learning,” in 2020 IEEE International conference on robotics and automation (ICRA) . Ieee, 2020, pp. 2414–2420

    work page 2020

  2. [1]

    Cardiovascular disease in europe: epidemiological update 2016,

    N. Townsend, L. Wilson, P. Bhatnagar, K. Wickramasinghe, M. Rayner, and M. Nichols, “Cardiovascular disease in europe: epidemiological update 2016,” European heart journal, vol. 37, no. 42, pp. 3232–3245, 2016

    work page 2016

  3. [2]

    Endovascular intervention for periph- eral artery disease,

    A. K. Thukkani and S. Kinlay, “Endovascular intervention for periph- eral artery disease,” Circulation research, vol. 116, no. 9, pp. 1599– 1613, 2015

    work page 2015

  4. [3]

    Endovascular thrombectomy after large- vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials,

    M. Goyal, B. K. Menon, W. H. Van Zwam, D. W. Dippel, P. J. Mitchell, A. M. Demchuk, A. D´avalos, C. B. Majoie, A. van Der Lugt, M. A. De Miquel, et al. , “Endovascular thrombectomy after large- vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials,” The Lancet , vol. 387, no. 10029, pp. 1723–1731, 2016

    work page 2016

  5. [4]

    Percutaneous coronary intervention vs coronary artery bypass grafting in patients with left main coronary artery stenosis: a systematic review and meta- analysis,

    D. Giacoppo, R. Colleran, S. Cassese, A. H. Frangieh, J. Wiebe, M. Joner, H. Schunkert, A. Kastrati, and R. A. Byrne, “Percutaneous coronary intervention vs coronary artery bypass grafting in patients with left main coronary artery stenosis: a systematic review and meta- analysis,” JAMA cardiology, vol. 2, no. 10, pp. 1079–1088, 2017

    work page 2017

  6. [5]

    Endovascular coiling versus neurosurgical clipping for people with aneurysmal subarachnoid haem- orrhage,

    A. Lindgren, M. D. Vergouwen, I. van der Schaaf, A. Algra, M. Wer- mer, M. J. Clarke, and G. J. Rinkel, “Endovascular coiling versus neurosurgical clipping for people with aneurysmal subarachnoid haem- orrhage,” Cochrane Database of Systematic Reviews , no. 8, 2018

    work page 2018

  7. [6]

    Brilakis, Manual of percutaneous coronary interventions: a step- by-step approach

    E. Brilakis, Manual of percutaneous coronary interventions: a step- by-step approach. Academic Press, 2020

    work page 2020

  8. [7]

    Time to treatment with endovascular thrombectomy and outcomes from ischemic stroke: a meta-analysis,

    J. L. Saver, M. Goyal, A. Van der Lugt, B. K. Menon, C. B. Majoie, D. W. Dippel, B. C. Campbell, R. G. Nogueira, A. M. Demchuk, A. Tomasello, et al., “Time to treatment with endovascular thrombectomy and outcomes from ischemic stroke: a meta-analysis,” Jama, vol. 316, no. 12, pp. 1279–1289, 2016

    work page 2016

Show all 61 references
  1. [8]

    Estimating the number of uk stroke patients eligible for endovascular thrombectomy,

    P. McMeekin, P. White, M. A. James, C. I. Price, D. Flynn, and G. A. Ford, “Estimating the number of uk stroke patients eligible for endovascular thrombectomy,” European stroke journal , vol. 2, no. 4, pp. 319–326, 2017

    2017

  2. [9]

    Nephrotoxicity of ionic and nonionic contrast media in 1196 patients: a randomized trial,

    M. R. Rudnick, S. Goldfarb, L. Wexler, P. A. Ludbrook, M. J. Murphy, E. F. Halpern, J. A. Hill, M. Winniford, M. B. Cohen, D. B. VanFossen, et al. , “Nephrotoxicity of ionic and nonionic contrast media in 1196 patients: a randomized trial,” Kidney international, vol. 47, no. 1...

    1995

  3. [10]

    Complications in endoluminal repair of abdominal aortic aneurysms,

    K. A. Hausegger, P. Schedlbauer, H. A. Deutschmann, and K. Tiesen- hausen, “Complications in endoluminal repair of abdominal aortic aneurysms,” European journal of radiology , vol. 39, no. 1, pp. 22– 33, 2001

    2001

  4. [11]

    Navigation with minimal occupation volume for teleoperated snake-like surgical robots: Move,

    P. Berthet-Rayne and G.-Z. Yang, “Navigation with minimal occupation volume for teleoperated snake-like surgical robots: Move,” Frontiers in Robotics and AI , vol. 10, 2023. [On- line]. Available: https://www.frontiersin.org/journals/robotics-and-ai/ articles/10.3389/frobt.202...

    2023

  5. [12]

    Trajectory optimization of robot-assisted endovas- cular catheterization with reinforcement learning,

    W. Chi, J. Liu, M. E. Abdelaziz, G. Dagnino, C. Riga, C. Bicknell, and G.-Z. Yang, “Trajectory optimization of robot-assisted endovas- cular catheterization with reinforcement learning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Ieee,...

    2018

  6. [13]

    Deep Reinforcement Learning for the Navigation of Neurovascular Catheters,

    T. Behr, T. P. Pusch, M. Siegfarth, D. H ¨usener, T. M ¨orschel, and L. Karstensen, “Deep Reinforcement Learning for the Navigation of Neurovascular Catheters,” Current Directions in Biomedical Engineer- ing, vol. 5, no. 1, pp. 5–8, Sept. 2019

    2019

  7. [14]

    Deep reinforcement learning for guidewire navigation in coronary artery phantom,

    J. Kweon, K. Kim, C. Lee, H. Kwon, J. Park, K. Song, Y . I. Kim, J. Park, I. Back, J.-H. Roh, et al. , “Deep reinforcement learning for guidewire navigation in coronary artery phantom,” IEEE Access, 2021

    2021

  8. [15]

    Sim-to-real transfer of image-based autonomous guidewire navigation trained by deep deterministic policy gradient with behavior cloning for fast learning,

    Y . Cho, J.-H. Park, J. Choi, and D. E. Chang, “Sim-to-real transfer of image-based autonomous guidewire navigation trained by deep deterministic policy gradient with behavior cloning for fast learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Syste...

    2022

  9. [16]

    Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver,

    L. Karstensen, J. Ritter, J. Hatzl, T. P ¨atz, J. Langej ¨urgen, C. Uhl, and F. Mathis-Ullrich, “Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver,” International Journal of Computer Assisted Radiology a...

    2022

  10. [17]

    Neurosurgery and artificial intelligence,

    M. Mofatteh, “Neurosurgery and artificial intelligence,” AIMS neuro- science, vol. 8, no. 4, p. 477, 2021

    2021

  11. [18]

    Robotics in neurointer- ventional surgery: a systematic review of the literature,

    W. Crinnion, B. Jackson, A. Sood, J. Lynch, C. Bergeles, H. Liu, K. Rhode, V . M. Pereira, and T. C. Booth, “Robotics in neurointer- ventional surgery: a systematic review of the literature,” Journal of neurointerventional surgery, vol. 14, no. 6, pp. 539–545, 2022

    2022

  12. [19]

    Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots,

    E. Fosch-Villaronga and T. Mahler, “Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots,” Computer Law & Security Review, vol. 41, p. 105528, 2021. [Online]. Available: https: //www.sciencedirect.com/science/a...

    2021

  13. [20]

    Toward a versatile robotic platform for fluoroscopy and mri-guided endovascular interventions: A pre-clinical study,

    M. E. M. K. Abdelaziz, D. Kundrat, M. Pupillo, G. Dagnino, et al. , “Toward a versatile robotic platform for fluoroscopy and mri-guided endovascular interventions: A pre-clinical study,” in IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS) , 2019

    2019

  14. [21]

    Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization,

    W. Chi, J. Liu, H. Rafii-Tari, C. Riga, C. Bicknell, and G.-Z. Yang, “Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization,” International journal of computer assisted radiology and surgery , vol. 13, pp. 85...

    2018

  15. [23]

    3-d respiratory motion compensation during ep procedures by image-based 3-d lasso catheter model generation and tracking,

    A. Brost, R. Liao, J. Hornegger, and N. Strobel, “3-d respiratory motion compensation during ep procedures by image-based 3-d lasso catheter model generation and tracking,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2009

    2009

  16. [24]

    Respiratory motion compensation by model-based catheter tracking during ep procedures,

    A. Brost, R. Liao, N. Strobel, and J. Hornegger, “Respiratory motion compensation by model-based catheter tracking during ep procedures,” Medical Image Analysis , vol. 14, no. 5, pp. 695–706, 2010

    2010

  17. [25]

    Guide-wire tracking during endovascular interventions,

    S. A. Baert, M. A. Viergever, and W. J. Niessen, “Guide-wire tracking during endovascular interventions,” IEEE Transactions on Medical Imaging, 2003

    2003

  18. [26]

    Learning-based hypothesis fusion for robust catheter tracking in 2d x-ray fluoroscopy,

    W. Wu, T. Chen, P. Wang, S. K. Zhou, D. Comaniciu, A. Barbu, and N. Strobel, “Learning-based hypothesis fusion for robust catheter tracking in 2d x-ray fluoroscopy,” in CVPR 2011 . IEEE, 2011, pp. 1097–1104

    2011

  19. [27]

    End-to-end real- time catheter segmentation with optical flow-guided warping during endovascular intervention,

    A. Nguyen, D. Kundrat, G. Dagnino, W. Chi, M. E. Abdelaziz, Y . Guo, Y . Ma, T. M. Kwok, C. Riga, and G.-Z. Yang, “End-to-end real- time catheter segmentation with optical flow-guided warping during endovascular intervention,” in 2020 IEEE International Conference on Robotics ...

    2020

  20. [28]

    Automatic detection of supporting device positioning in intensive care unit radiography,

    C. Sheng, L. Li, and W. Pei, “Automatic detection of supporting device positioning in intensive care unit radiography,” The International Journal of Medical Robotics and Computer Assisted Surgery , vol. 5, no. 3, pp. 332–340, 2009

    2009

  21. [29]

    Auto- mated detection of endotracheal tubes in paediatric chest radiographs,

    E.-F. Kao, T.-S. Jaw, C.-W. Li, M.-C. Chou, and G.-C. Liu, “Auto- mated detection of endotracheal tubes in paediatric chest radiographs,” Computer Methods and Programs in Biomedicine , 2015

    2015

  22. [30]

    Semi-automated location identification of catheters in digital chest radiographs,

    B. M. Keller, A. P. Reeves, M. D. Cham, C. I. Henschke, and D. F. Yankelevitz, “Semi-automated location identification of catheters in digital chest radiographs,” in Medical Imaging 2007: Computer-Aided Diagnosis, vol. 6514. SPIE, 2007, pp. 528–536

    2007

  23. [31]

    Curvilinear struc- ture enhancement with the polygonal path image-application to guide- wire segmentation in x-ray fluoroscopy,

    V . Bismuth, R. Vaillant, H. Talbot, and L. Najman, “Curvilinear struc- ture enhancement with the polygonal path image-application to guide- wire segmentation in x-ray fluoroscopy,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Spr...

    2012

  24. [32]

    A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures,

    Y . Ma, M. Alhrishy, S. A. Narayan, P. Mountney, and K. S. Rhode, “A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures,” Medical Physics, 2018

    2018

  25. [33]

    Shape-sensitive loss for catheter and guidewire segmentation,

    C. Kongtongvattana, B. Huang, J. Kang, H. Nguyen, O. Olufemi, and A. Nguyen, “Shape-sensitive loss for catheter and guidewire segmentation,” in International Conference on Robot Intelligence Technology and Applications . Springer, 2023, pp. 95–107

    2023

  26. [34]

    Robust guidewire tracking in fluoroscopy,

    P. Wang, T. Chen, Y . Zhu, W. Zhang, S. K. Zhou, and D. Comaniciu, “Robust guidewire tracking in fluoroscopy,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2009, pp. 691– 698

    2009

  27. [35]

    Guidewire tracking using a novel sequential segment optimization method in interven- tional x-ray videos,

    B.-J. Chen, Z. Wu, S. Sun, D. Zhang, and T. Chen, “Guidewire tracking using a novel sequential segment optimization method in interven- tional x-ray videos,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2016, pp. 103–106

    2016

  28. [36]

    Guide-wire detection using region proposal network for x-ray image-guided navigation,

    L. Wang, X.-L. Xie, G.-B. Bian, Z.-G. Hou, X.-R. Cheng, and P. Prasong, “Guide-wire detection using region proposal network for x-ray image-guided navigation,” in International Joint Conference on Neural Networks (IJCNN) , 2017

    2017

  29. [37]

    A machine learning approach for deformable guide-wire tracking in fluoroscopic sequences,

    O. Pauly, H. Heibel, and N. Navab, “A machine learning approach for deformable guide-wire tracking in fluoroscopic sequences,” in In- ternational Conference on Medical Image Computing and Computer- Assisted Intervention , 2010

    2010

  30. [38]

    Improving catheter segmentation & localization in 3d cardiac ultrasound using direction- fused fcn,

    H. Yang, C. Shan, A. F. Kolen, and P. H. de With, “Improving catheter segmentation & localization in 3d cardiac ultrasound using direction- fused fcn,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . IEEE, 2019, pp. 1122–1126

    2019

  31. [39]

    Fully automatic catheter segmentation in mri with 3d convolutional neural networks: application to mri-guided gynecologic brachytherapy,

    P. Zaffino, G. Pernelle, A. Mastmeyer, A. Mehrtash, H. Zhang, R. Kikinis, T. Kapur, and M. F. Spadea, “Fully automatic catheter segmentation in mri with 3d convolutional neural networks: application to mri-guided gynecologic brachytherapy,” Physics in Medicine & Biology, vol. ...

    2019

  32. [40]

    U-net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, pro- ceedings, part III 18 ...

    2015

  33. [41]

    Automatic catheter and tube detection in pediatric x-ray images using a scale-recurrent network and synthetic data,

    X. Yi, S. Adams, P. Babyn, and A. Elnajmi, “Automatic catheter and tube detection in pediatric x-ray images using a scale-recurrent network and synthetic data,” Journal of Digital Imaging , 2019

    2019

  34. [42]

    Fully automatic and real-time catheter segmentation in x- ray fluoroscopy,

    P. Ambrosini, D. Ruijters, W. J. Niessen, A. Moelker, and T. van Walsum, “Fully automatic and real-time catheter segmentation in x- ray fluoroscopy,” in Medical Image Computing and Computer-Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Can...

    2017

  35. [43]

    Real-time guidewire tracking and segmentation in intraoperative x- ray,

    B. Zhang, M. Bui, C. Wang, F. Bourier, H. Schunkert, and N. Navab, “Real-time guidewire tracking and segmentation in intraoperative x- ray,” in Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling , vol. 12034. SPIE, 2022, pp. 283–290

    2022

  36. [44]

    Self-supervised depth esti- mation in laparoscopic image using 3d geometric consistency,

    B. Huang, J.-Q. Zheng, A. Nguyen, C. Xu, I. Gkouzionis, K. Vyas, D. Tuch, S. Giannarou, and D. S. Elson, “Self-supervised depth esti- mation in laparoscopic image using 3d geometric consistency,” in In- ternational Conference on Medical Image Computing and Computer- Assisted I...

    2022

  37. [45]

    Simultaneous depth estimation and surgical tool segmentation in laparoscopic images,

    B. Huang, A. Nguyen, S. Wang, Z. Wang, E. Mayer, D. Tuch, K. Vyas, S. Giannarou, and D. S. Elson, “Simultaneous depth estimation and surgical tool segmentation in laparoscopic images,” IEEE transactions on medical robotics and bionics , vol. 4, no. 2, pp. 335–338, 2022

    2022

  38. [46]

    Cathsim: an open- source simulator for endovascular intervention,

    T. Jianu, B. Huang, M. N. Vu, M. E. Abdelaziz, S. Fichera, C.-Y . Lee, P. Berthet-Rayne, F. R. y Baena, and A. Nguyen, “Cathsim: an open- source simulator for endovascular intervention,” IEEE Transactions on Medical Robotics and Bionics , 2024

    2024

  39. [47]

    Automatic control of cardiac ablation catheter with deep reinforcement learning method,

    H. You, E. Bae, Y . Moon, J. Kweon, and J. Choi, “Automatic control of cardiac ablation catheter with deep reinforcement learning method,” Journal of Mechanical Science and Technology , vol. 33, pp. 5415– 5423, 2019

    2019

  40. [48]

    Autonomous catheter- ization with open-source simulator and expert trajectory,

    T. Jianu, B. Huang, T. V o, M. N. Vu, J. Kang, H. Nguyen, O. Omisore, P. Berthet-Rayne, S. Fichera, and A. Nguyen, “Autonomous catheter- ization with open-source simulator and expert trajectory,” arXiv preprint arXiv:2401.09059, 2024

    2024 arXiv

  41. [49]

    A novel Master-Slave interventional surgery robot with force feedback and collaborative operation,

    Y . Song, L. Li, Y . Tian, Z. Li, and X. Yin, “A novel Master-Slave interventional surgery robot with force feedback and collaborative operation,” Sensors (Basel) , vol. 23, no. 7, Mar. 2023

    2023

  42. [50]

    Novel robotic catheter remote control system: feasibility and safety of transseptal puncture and endocardial catheter navigation,

    W. Saliba, J. E. Cummings, S. Oh, Y . Zhang, T. N. Mazgalev, R. A. Schweikert, J. D. Burkhardt, and A. Natale, “Novel robotic catheter remote control system: feasibility and safety of transseptal puncture and endocardial catheter navigation,” Journal of cardiovascular elec- tr...

    2006

  43. [51]

    First experience with a novel robotic remote catheter system: Amigo™ mapping trial,

    E. M. Khan, W. Frumkin, G. A. Ng, S. Neelagaru, F. M. Abi-Samra, J. Lee, M. Giudici, D. Gohn, R. A. Winkle, J. Sussman, et al. , “First experience with a novel robotic remote catheter system: Amigo™ mapping trial,” Journal of Interventional Cardiac Electrophysiology , vol. 37,...

    2013

  44. [52]

    Martin, Y

    J.-B. Martin, Y . Sayegh, P. Gailloud, K. Sugiu, H. G. Khan, J. H. Fasel, and D. A. R ¨ufenacht, In-vitro models of human carotid atheromatous disease, 1998

    1998

  45. [53]

    In vitro models of intracranial arteriovenous fistulas for the evaluation of new endovascular treatment materials,

    P. Gailloud, M. Muster, M. Piotin, F. Mottu, K. J. Murphy, J. H. Fasel, and D. A. R ¨ufenacht, “In vitro models of intracranial arteriovenous fistulas for the evaluation of new endovascular treatment materials,” AJNR, 1999

    1999

  46. [54]

    Computer vision annotation tool (cvat),

    CV AT.ai Corporation, “Computer vision annotation tool (cvat),” Nov

  47. [55]

    First experience in colorectal surgery with a new robotic platform with haptic feedback,

    A. Spinelli, G. David, S. Gidaro, M. Carvello, M. Sacchi, M. Montorsi, and I. Montroni, “First experience in colorectal surgery with a new robotic platform with haptic feedback,” Colorectal Disease, 2018

    2018

  48. [56]

    Robust catheter and guidewire tracking using b-spline tube model and pixel-wise posteriors,

    P.-L. Chang, A. Rolls, H. D. Praetere, E. V . Poorten, C. V . Riga, C. D. Bicknell, and D. Stoyanov, “Robust catheter and guidewire tracking using b-spline tube model and pixel-wise posteriors,” IEEE Robotics and Automation Letters , vol. 1, no. 1, pp. 303–308, 2016

    2016

  49. [57]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly,et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

    2010 arXiv

  50. [58]

    Attention is all you need,

    A. Vaswani, “Attention is all you need,” Advances in Neural Informa- tion Processing Systems , 2017

    2017

  51. [59]

    Multispeech: Multi-speaker text to speech with transformer,

    M. Chen, X. Tan, Y . Ren, J. Xu, H. Sun, S. Zhao, T. Qin, and T.- Y . Liu, “Multispeech: Multi-speaker text to speech with transformer,” arXiv preprint arXiv:2006.04664 , 2020

    2006 arXiv

  52. [60]

    Cptr: Full transformer network for image captioning,

    W. Liu, S. Chen, L. Guo, X. Zhu, and J. Liu, “Cptr: Full transformer network for image captioning,” arXiv preprint arXiv:2101.10804 , 2021

    2021 arXiv

  53. [2023]

    Available: https://cvat.ai/

    [Online]. Available: https://cvat.ai/

Pith tools

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