Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video

Keyun Cheng; Pheng-Ann Heng; Qi Dou; Yueming Jin

arxiv: 1907.07899 · v1 · pith:OOXOG2NNnew · submitted 2019-07-18 · 💻 cs.CV

Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video

Yueming Jin , Keyun Cheng , Qi Dou , Pheng-Ann Heng This is my paper

Pith reviewed 2026-05-24 20:01 UTC · model grok-4.3

classification 💻 cs.CV

keywords instrument segmentationtemporal priormotion flowattention pyramid networkminimally invasive surgerysemi-supervised learningendoscopic videorobotic instrument segmentation

0 comments

The pith

A temporal prior from motion flow, injected into attention modules, improves instrument segmentation accuracy in surgical videos.

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

The paper establishes that motion flow between video frames can generate a reliable prior on the location and shape of surgical instruments in the current frame. This prior initializes a pyramid of attention modules inside an encoder-decoder network, guiding segmentation from coarse to fine scales while letting temporal information and attention reinforce each other. The resulting method is tested on the public EndoVis Robotic Instrument Segmentation Challenge dataset and outperforms prior approaches on three separate tasks. The same prior mechanism also supports semi-supervised training by propagating information backward through unlabeled frames. Such segmentation accuracy matters for building reliable robotic assistance tools that can track and interact with instruments during procedures.

Core claim

The central claim is that an inferred temporal prior, obtained by propagating instrument location and shape from the previous frame to the current frame according to inter-frame motion flow, can be injected as initialization into the middle of an encoder-decoder segmentation network at the start of a pyramid of attention modules, thereby explicitly guiding output from coarse to fine and allowing temporal dynamics and attention to complement each other.

What carries the argument

The temporal prior derived from inter-frame motion flow, which supplies an initial estimate of instrument location and shape that initializes the pyramid of attention modules inside the encoder-decoder network.

If this is right

Segmentation exceeds state-of-the-art results on all three tasks of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge.
Semi-supervised learning becomes feasible by reverse execution on video frames that lack labels.
Annotation effort in clinical practice can be lowered because the temporal prior reduces the need for dense labeling of every frame.
Temporal motion cues and attention mechanisms inside the network mutually improve segmentation output.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same prior-propagation idea could be tested on other video segmentation problems outside surgery where object motion is predictable.
Performance may degrade in procedures with very different motion statistics, such as those involving deformable tissue rather than rigid instruments.
Replacing the motion-flow step with a learned flow network might further stabilize the prior under challenging lighting.

Load-bearing premise

Motion flow estimation stays accurate enough to propagate a useful prior even when the video contains occlusions, specular reflections, and fast tool motion.

What would settle it

Run the method on EndoVis sequences where independent optical-flow error is measured to be high; if segmentation accuracy then falls below the non-temporal baseline, the prior-injection benefit does not hold.

Figures

Figures reproduced from arXiv: 1907.07899 by Keyun Cheng, Pheng-Ann Heng, Qi Dou, Yueming Jin.

**Figure 1.** Figure 1: Illustration of the proposed (a) MF-TAPNet for surgical instrument segmentation based on motion flow, with architecture of (b) temporal attention pyramid network and (c) attention guided module presented in detail. 2.1 Unsupervised Temporal Propagation via Motion Flow In surgical video, instruments performed by surgeons, usually have obvious and rich motion information. Such valuable temporal inherence in… view at source ↗

**Figure 2.** Figure 2: Typical results for instrument (a) binary segmentation (instrument and background tissues), (b) part segmentation (shaft, wrist and jaws), (c) type segmentation (different yet looking quite similar instruments). From top to bottom, for each task, we present two continuous video frames and their corresponding ground truth, with segmentation results using PlainNet, TAPNet and our proposed MF-TAPNet. when un… view at source ↗

read the original abstract

Automatic instrument segmentation in video is an essentially fundamental yet challenging problem for robot-assisted minimally invasive surgery. In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation. Our inferred prior can provide reliable indication of the instrument location and shape, which is propagated from the previous frame to the current frame according to inter-frame motion flow. This prior is injected to the middle of an encoder-decoder segmentation network as an initialization of a pyramid of attention modules, to explicitly guide segmentation output from coarse to fine. In this way, the temporal dynamics and the attention network can effectively complement and benefit each other. As additional usage, our temporal prior enables semi-supervised learning with periodically unlabeled video frames, simply by reverse execution. We extensively validate our method on the public 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset with three different tasks. Our method consistently exceeds the state-of-the-art results across all three tasks by a large margin. Our semi-supervised variant also demonstrates a promising potential for reducing annotation cost in the clinical practice.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper adds a motion-flow prior to seed attention modules in a segmentation net for surgical instruments, but the abstract's performance claims rest on unvalidated flow accuracy under typical endoscopic artifacts.

read the letter

The core contribution is a practical mechanism: derive a temporal prior by warping the previous frame's instrument mask via optical flow, then feed that as initialization into a pyramid of attention blocks inside an encoder-decoder. They also note a semi-supervised use case by reversing the process on unlabeled frames. That combination is not in the cited prior work and gives a clear way to inject temporal consistency without redesigning the whole network. The approach stays grounded in standard tools (flow estimation plus attention) and targets a real clinical need where frame-to-frame motion is available. The abstract reports consistent large-margin gains over SOTA on the three EndoVis tasks, which would be useful if the numbers hold. The main weakness is that the prior depends on flow remaining accurate despite specular reflections, smoke, and fast tool motion. No mention of flow endpoint error, an ablation with ground-truth flow, or qualitative failure cases on bad-flow frames. Without those checks it is difficult to attribute the reported gains to the temporal prior rather than the base network or training details. The paper is aimed at researchers in medical video segmentation who already work with attention or temporal models. A reader looking for concrete implementation ideas on propagating shape priors could extract value even if the results section needs closer inspection. I would send this to peer review because the technical step is well-defined and the dataset is public, so referees can verify the numbers and the flow assumption directly.

Referee Report

2 major / 2 minor

Summary. The paper proposes a framework for instrument segmentation in minimally invasive surgery videos that derives a temporal prior by propagating instrument location and shape from the previous frame via inter-frame motion flow, then injects this prior as initialization into a pyramid of attention modules within an encoder-decoder network. The temporal prior and attention components are said to complement each other; the approach also supports semi-supervised learning via reverse execution on unlabeled frames. The central claim is consistent large-margin outperformance over state-of-the-art on all three tasks of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset.

Significance. If the performance gains can be confidently attributed to the temporal prior after proper validation of the motion-flow component, the work would offer a practical way to exploit video dynamics in surgical scenes and reduce annotation burden via the semi-supervised variant. The combination of flow-based propagation with attention pyramids is a reasonable design choice for this domain, but the absence of supporting evidence for the load-bearing assumption limits the assessed impact.

major comments (2)

[Abstract / Results] Abstract and Results section: the claim that the method 'consistently exceeds the state-of-the-art results across all three tasks by a large margin' is presented without any quantitative metrics, tables, or error analysis in the abstract and is not accompanied by the numerical evidence needed to evaluate magnitude or consistency.
[Method] Method description (temporal prior propagation): the assumption that 'the inferred prior can provide reliable indication of the instrument location and shape' propagated by motion flow is load-bearing for the performance claim, yet no flow endpoint error, ablation with ground-truth flow, or analysis on frames with specular highlights/occlusions/fast motion is reported. This leaves open whether gains arise from the prior or from the base attention network.

minor comments (2)

[Abstract] Abstract: the three tasks are referenced but never named or briefly characterized.
[Method] Notation: the injection of the prior into the attention pyramid would benefit from an explicit equation or diagram label showing how the prior initializes the pyramid modules.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate where revisions will be made.

read point-by-point responses

Referee: [Abstract / Results] Abstract and Results section: the claim that the method 'consistently exceeds the state-of-the-art results across all three tasks by a large margin' is presented without any quantitative metrics, tables, or error analysis in the abstract and is not accompanied by the numerical evidence needed to evaluate magnitude or consistency.

Authors: We agree that the abstract would benefit from explicit numerical support for the performance claim. While the Results section includes full tables with metrics and comparisons to prior methods, we will revise the abstract to include key quantitative values (e.g., Dice/IoU margins over the previous state-of-the-art) to allow immediate evaluation of the reported improvements. revision: yes
Referee: [Method] Method description (temporal prior propagation): the assumption that 'the inferred prior can provide reliable indication of the instrument location and shape' propagated by motion flow is load-bearing for the performance claim, yet no flow endpoint error, ablation with ground-truth flow, or analysis on frames with specular highlights/occlusions/fast motion is reported. This leaves open whether gains arise from the prior or from the base attention network.

Authors: The contribution of the temporal prior is supported by the consistent gains across tasks and the semi-supervised results, but we acknowledge the absence of dedicated flow validation. We will add an ablation isolating the prior (with vs. without) and a qualitative/quantitative analysis on frames exhibiting specular highlights, occlusions, and fast motion. Ground-truth optical flow is unavailable in the EndoVis dataset, so a GT-flow ablation cannot be performed. revision: partial

standing simulated objections not resolved

Ablation with ground-truth flow, as the EndoVis dataset provides no ground-truth optical flow.

Circularity Check

0 steps flagged

No circularity; derivation uses standard optical flow and attention without self-referential reduction

full rationale

The paper's method derives a temporal prior by propagating instrument location and shape via inter-frame motion flow and injects it as initialization into an attention pyramid network. No equations, self-definitions, or fitted parameters presented as predictions appear in the abstract or described chain. The approach relies on established components (optical flow estimation and attention modules) with empirical validation on the EndoVis dataset. No self-citation chains or uniqueness theorems are invoked as load-bearing. The central claim of performance gains is not reduced to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that inter-frame motion flow yields a reliable prior for instrument location despite endoscopic artifacts; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Inter-frame motion flow can be reliably estimated and used to propagate instrument location and shape from previous to current frame in endoscopic video.
Invoked when the abstract states the prior is propagated according to motion flow to provide reliable indication of location and shape.

pith-pipeline@v0.9.0 · 5730 in / 1222 out tokens · 19717 ms · 2026-05-24T20:01:30.818763+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 4 internal anchors

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IEEE TMI 37(5), 1204–1213 (2018)

Allan, M., Ourselin, S., et al.: 3-D pose estimation of articulated instruments in robotic minimally invasive surgery. IEEE TMI 37(5), 1204–1213 (2018)

work page 2018

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2017 Robotic Instrument Segmentation Challenge

Allan, M., Shvets, A., et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)

work page internal anchor Pith review Pith/arXiv arXiv 2017

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IEEE TMI 34(12), 2603–2617 (2015)

Bouget, D., Benenson, R., et al.: Detecting surgical tools by modelling local ap- pearance and global shape. IEEE TMI 34(12), 2603–2617 (2015)

work page 2015

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In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´ opez, C., Fichtinger, G

Chen, J., Yang, G., et al.: Multiview two-task recursive attention model for left atrium and atrial scars segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´ opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 455–463. Springer (2018). https://doi.org/10.1007/978-3-030-00934-2

work page doi:10.1007/978-3-030-00934-2 2018

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In: IEEE/RSJ IROS

Garc´ ıa-Peraza-Herrera, L.C., Li, W., et al.: ToolNet: holistically-nested real-time segmentation of robotic surgical tools. In: IEEE/RSJ IROS. pp. 5717–5722 (2017)

work page 2017

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U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument

Hasan, S., Linte, C.A.: U-NetPlus: a modiﬁed encoder-decoder u-net architecture for semantic and instance segmentation of surgical instrument. arXiv preprint arXiv:1902.08994 (2019)

work page internal anchor Pith review Pith/arXiv arXiv 1902

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IEEE TMI 37(5), 1114–1126 (2018)

Jin, Y., Dou, Q., et al.: SV-RCNet: workﬂow recognition from surgical videos using recurrent convolutional network. IEEE TMI 37(5), 1114–1126 (2018)

work page 2018

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Adam: A Method for Stochastic Optimization

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014) Incorporating Temporal Prior for Surgical Instrument Segmentation 9

work page internal anchor Pith review Pith/arXiv arXiv 2014

[9] [9]

In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S

Laina, I., Rieke, N., et al.: Concurrent segmentation and localization for tracking of surgical instruments. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 664–672. Springer (2017). https://doi.org/10.1007/978-3-319-66185-8

work page doi:10.1007/978-3-319-66185-8 2017

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In: AAAI (2018)

Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical ﬂow with a bidirectional census loss. In: AAAI (2018)

work page 2018

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In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´ opez, C., Fichtinger, G

Milletari, F., Rieke, N., et al.: CFCM: segmentation via coarse to ﬁne context memory. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L´ opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 667–674. Springer (2018). https://doi.org/10.1007/978-3-030-00937-3

work page doi:10.1007/978-3-030-00937-3 2018

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MIDL (2018)

Oktay, O., Schlemper, J., et al.: Attention U-Net: learning where to look for the pancreas. MIDL (2018)

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Medical Image Analysis 34, 82–100 (2016)

Rieke, N., Tan, D.J., et al.: Real-time localization of articulated surgical instru- ments in retinal microsurgery. Medical Image Analysis 34, 82–100 (2016)

work page 2016

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In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomed- ical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer (2015). https://doi.org/10.1007/978-3-319-24574-4

work page doi:10.1007/978-3-319-24574-4 2015

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IEEE TMI 36(7), 1542–1549 (2017)

Sarikaya, D., Corso, J.J., Guru, K.A.: Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection. IEEE TMI 36(7), 1542–1549 (2017)

work page 2017

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In: ICMLA

Shvets, A.A., Rakhlin, A., et al.: Automatic instrument segmentation in robot- assisted surgery using deep learning. In: ICMLA. pp. 624–628 (2018)

work page 2018

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Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

work page internal anchor Pith review Pith/arXiv arXiv 2014

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IEEE TMI 36(1), 86–97 (2017)

Twinanda, A.P., Shehata, S., et al.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE TMI 36(1), 86–97 (2017)

work page 2017