arxiv: 2604.09151 · v1 · submitted 2026-04-10 · 💻 cs.CV · nlin.PS

Recognition: no theorem link

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery

Sara Ameli

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:32 UTC · model grok-4.3

classification 💻 cs.CV nlin.PS

keywords surgical instrument segmentationrobotic-assisted surgerysemantic segmentationDeepLabV3SegFormerSAR-RARP50CNN vs transformermulti-class segmentation

0 comments

The pith

DeepLabV3 matches SegFormer performance in segmenting surgical instruments from robotic prostatectomy videos.

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

This paper benchmarks five deep learning models for multi-class semantic segmentation of surgical instruments in real-world radical prostatectomy videos from the SAR-RARP50 dataset. The models include convolutional architectures such as UNet, DeepLabV3, and Attention UNet alongside the transformer-based SegFormer, all trained with a compound cross-entropy and Dice loss to manage class imbalance and boundary precision. The central result is that DeepLabV3 reaches accuracy levels comparable to SegFormer by leveraging atrous convolutions and multi-scale context aggregation to handle complex surgical scenes. A reader would care because accurate instrument segmentation directly enables downstream tasks like tool tracking, workflow analysis, and context-aware interventions in robotic surgery. The study also surfaces practical trade-offs between convolutional efficiency and transformer global context for selecting models in surgical AI applications.

Core claim

In benchmarking UNet, DeepLabV3, Attention UNet, and SegFormer on the SAR-RARP50 dataset for multi-class semantic segmentation of surgical instruments in radical prostatectomy videos, DeepLabV3 achieves results comparable to SegFormer. This outcome demonstrates the effectiveness of atrous convolution and multi-scale context aggregation in capturing complex surgical scenes, while transformer-based architectures like SegFormer further enhance global contextual understanding and generalization across varying instrument appearances and surgical conditions.

What carries the argument

Benchmark comparison of DeepLabV3's atrous convolution with multi-scale aggregation against SegFormer's transformer encoder for global context in surgical instrument segmentation on the SAR-RARP50 dataset.

If this is right

Convolutional models equipped with atrous operations can serve as efficient alternatives to transformers when global context is not strictly required.
Multi-scale feature aggregation proves sufficient to manage variability in instrument shapes, lighting, and occlusion during live procedures.
Model selection for surgical AI can favor convolutional approaches for lower compute when benchmark parity holds.
Transformer architectures may deliver their largest gains only under broader variation in surgical conditions or instrument types.

Where Pith is reading between the lines

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

Real-time deployment in operating rooms could prefer DeepLabV3-style CNNs to lower latency and hardware requirements during procedures.
Extending the benchmark to other robotic surgery procedures such as cholecystectomy would test whether the observed parity generalizes.
Hybrid architectures that insert atrous layers into transformer backbones might combine the strengths without full model replacement.

Load-bearing premise

The SAR-RARP50 dataset captures enough real-world variability in radical prostatectomy videos that performance differences between models reflect their inherent capabilities rather than dataset quirks or training choices.

What would settle it

Retraining the same five models on a new, independent set of robotic surgery videos with identical loss and hyperparameters, then checking whether DeepLabV3 still matches or exceeds SegFormer in mean intersection-over-union scores.

Figures

Figures reproduced from arXiv: 2604.09151 by Sara Ameli.

**Figure 2.** Figure 2: Input frame and predicted segmentation using UNet– Video 41 Frame 0 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Figure 3: Input frame and predicted segmentation using UNet– Video 42 Frame 10 [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Per-class Dice coefficients on validation set suing U-Net [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

read the original abstract

Accurate segmentation of surgical instruments in robotic-assisted surgery is critical for enabling context-aware computer-assisted interventions, such as tool tracking, workflow analysis, and autonomous decision-making. In this study, we benchmark five deep learning architectures-UNet, UNet, DeepLabV3, Attention UNet, and SegFormer on the SAR-RARP50 dataset for multi-class semantic segmentation of surgical instruments in real-world radical prostatectomy videos. The models are trained with a compound loss function combining Cross Entropy and Dice loss to address class imbalance and capture fine object boundaries. Our experiments reveal that while convolutional models such as UNet and Attention UNet provide strong baseline performance, DeepLabV3 achieves results comparable to SegFormer, demonstrating the effectiveness of atrous convolution and multi-scale context aggregation in capturing complex surgical scenes. Transformer-based architectures like SegFormer further enhance global contextual understanding, leading to improved generalization across varying instrument appearances and surgical conditions. This work provides a comprehensive comparison and practical insights for selecting segmentation models in surgical AI applications, highlighting the trade-offs between convolutional and transformer-based approaches.

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

Standard benchmark of existing segmentation models on one surgical dataset, with thin support for the interpretive claims about DeepLabV3.

read the letter

This paper benchmarks five standard segmentation architectures on the SAR-RARP50 dataset for multi-class instrument segmentation in robotic prostatectomy videos. The main finding is that DeepLabV3 achieves results comparable to SegFormer using a compound loss. What it does well is provide a direct comparison between convolutional and transformer models in a real surgical setting, which can offer practical guidance for model selection in computer-assisted interventions. The work is not new in any technical sense. All models and the loss function are established, and the dataset is already public. It is an application of existing tools. The soft spots are noticeable. The abstract states comparative results but supplies no numbers, error bars, or tests, making it impossible to verify the claims. The stress-test concern is on point: the comparability does not isolate the contributions of atrous convolution and multi-scale context from other factors like the shared loss or training choices, since no module ablations are described. The weakest assumption is that the single dataset and standard training reflect true model differences rather than specifics of this setup. Reproducibility suffers from missing implementation details. This paper is for applied researchers in surgical robotics and medical image analysis who might use it as a reference for these models on this data. A reader seeking groundbreaking ideas will not find them. It deserves peer review. The area is relevant, and with proper results and experiments added, it could be a solid incremental contribution.

Referee Report

3 major / 1 minor

Summary. The paper benchmarks five deep learning architectures (UNet, DeepLabV3, Attention UNet, SegFormer) for multi-class semantic segmentation of surgical instruments on the SAR-RARP50 dataset of real-world radical prostatectomy videos. All models are trained with the same compound Cross-Entropy + Dice loss to handle class imbalance and boundaries. The central claim is that DeepLabV3 achieves performance comparable to the transformer-based SegFormer, demonstrating the value of atrous convolutions and multi-scale context aggregation, while SegFormer provides superior global context and generalization.

Significance. If the quantitative results, controls, and statistical support hold, the work would supply practical guidance on model selection for surgical instrument segmentation, clarifying trade-offs between CNN efficiency and transformer context modeling in robotic-assisted surgery. The use of a public dataset and standard compound loss aids reproducibility.

major comments (3)

[Abstract] Abstract: the claim that DeepLabV3 'achieves results comparable to SegFormer' is stated without any IoU, Dice, or other quantitative scores, error bars, or statistical tests, which is load-bearing for the paper's central benchmarking conclusion.
[Experimental setup] Experimental setup (training protocol): all five models share the same compound loss but no ablations, matched hyperparameter searches, or capacity controls are described to isolate the contribution of atrous convolutions and ASPP-style multi-scale aggregation in DeepLabV3 from other factors; this directly undermines the attribution of comparability to those components.
[Results] Results section: no details are supplied on training/validation splits, cross-validation strategy, or external test sets, so it is impossible to assess whether reported differences reflect true model capabilities or dataset-specific artifacts as assumed in the abstract.

minor comments (1)

[Abstract] Abstract: the architecture list repeats 'UNet' twice; clarify whether this refers to two distinct variants or is a typographical error.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights opportunities to strengthen the quantitative support, experimental controls, and methodological transparency in our benchmarking study. We address each major comment below and outline the corresponding revisions.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that DeepLabV3 'achieves results comparable to SegFormer' is stated without any IoU, Dice, or other quantitative scores, error bars, or statistical tests, which is load-bearing for the paper's central benchmarking conclusion.

Authors: We agree that the abstract would benefit from explicit metrics to support the comparability claim. In the revised manuscript, we will incorporate the mean IoU and Dice scores for DeepLabV3 and SegFormer (with standard deviations across runs) directly into the abstract, along with a brief note on the absence of statistically significant differences. revision: yes
Referee: [Experimental setup] Experimental setup (training protocol): all five models share the same compound loss but no ablations, matched hyperparameter searches, or capacity controls are described to isolate the contribution of atrous convolutions and ASPP-style multi-scale aggregation in DeepLabV3 from other factors; this directly undermines the attribution of comparability to those components.

Authors: All models were trained under identical conditions using the same compound loss, optimizer, learning rate schedule, and data augmentations, with hyperparameters tuned independently on the validation set for each architecture to ensure a fair comparison. We did not include module-specific ablations (e.g., removing ASPP from DeepLabV3) as the study focuses on end-to-end benchmarking rather than component dissection; however, we will add a dedicated paragraph in the Methods section referencing the original DeepLabV3 design choices and clarifying the hyperparameter selection process. revision: partial
Referee: [Results] Results section: no details are supplied on training/validation splits, cross-validation strategy, or external test sets, so it is impossible to assess whether reported differences reflect true model capabilities or dataset-specific artifacts as assumed in the abstract.

Authors: We apologize for the lack of explicit detail in the current draft. The Methods section describes use of the official SAR-RARP50 train/test split (with 20% of training videos held out for validation), but we will expand this with precise ratios, confirmation that no cross-validation was applied due to compute limits, and a statement that no external test sets were used beyond the dataset's provided test partition. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmarking with no derivations or self-referential reductions

full rationale

The paper performs a standard empirical comparison of five segmentation models (UNet, DeepLabV3, Attention UNet, SegFormer) trained with the same compound loss on the SAR-RARP50 dataset. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described methodology. The claim that DeepLabV3 demonstrates effectiveness of atrous convolution is presented as an interpretation of experimental results rather than a mathematical reduction to inputs. The study is self-contained against external benchmarks and contains no steps that reduce by construction to their own definitions or prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the representativeness of the SAR-RARP50 dataset and the assumption that standard deep learning training practices transfer without hidden biases to this surgical domain.

axioms (2)

domain assumption The SAR-RARP50 dataset is representative of real-world surgical conditions and class distributions.
Implicit in all reported generalization claims.
domain assumption Compound Cross Entropy plus Dice loss adequately addresses class imbalance without introducing evaluation bias.
Stated as the training approach but not validated in abstract.

pith-pipeline@v0.9.0 · 5482 in / 1255 out tokens · 65249 ms · 2026-05-10T17:32:40.233345+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 3 canonical work pages · 2 internal anchors

[1]

Herron, D. M. (2020). Robotic surgery: current applications and future trends.American Journal of Surgery, 220(4), 682–686

2020
[2]

A., Kurmann, T., Zhang, C., Duggal, R., Lee, D.,

Allan, M., Shvets, A. A., Kurmann, T., Zhang, C., Duggal, R., Lee, D., ... & Rieke, N. (2021). A comprehensive dataset for robotic instrument segmentation and tracking on clinically relevant data (SAR- RARP50).Medical Image Analysis, 70, 102002

2021
[3]

S., Speidel, S., Navab, N., Kikinis, R., Park, A.,

Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., ... & Taylor, R. H. (2017). Surgical data science for next-generation interventions.Nature Biomedical Engineering, 1(9), 691–696

2017
[4]

U-Net: Convolutional Networks for Biomedical Image Segmenta- tion

O. Ronneberger, P. Fischer, T. Brox. “U-Net: Convolutional Networks for Biomedical Image Segmenta- tion.” *MICCAI*, 2015

2015
[5]

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

L. Chen et al., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” *ECCV*, 2018

2018
[6]

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A Nested U-Net Archi- tecture for Medical Image Segmentation. InDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer

2018
[7]

A Comprehensive Dataset and Benchmarks for Surgical Instrument Segmentation

M. Allan et al., “A Comprehensive Dataset and Benchmarks for Surgical Instrument Segmentation.” *Medical Image Analysis*, 2021

2021
[8]

SAR-RARP50 Challenge:https://www.synapse.org/Synapse:syn27618412/wiki/616881
[10]

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

J. Chen et al., “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.” arXiv:2102.04306

work page internal anchor Pith review arXiv
[11]

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

A. P. Twinanda et al., “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.” *IEEE TMI*, 2016

2016
[12]

U-Net: Convolutional Networks for Biomedical Image Seg- mentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Seg- mentation,” inProc. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241, Springer, 2015

2015
[13]

UNet++: A Nested U-Net Archi- tecture for Medical Image Segmentation,

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A Nested U-Net Archi- tecture for Medical Image Segmentation,” inDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11, Springer, 2018

2018
[14]

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” inProceedings of the European Conference on Computer Vision (ECCV), pp. 801–818, 2018

2018
[15]

Attention U-Net: Learning Where to Look for the Pancreas

O. Oktay, J. Schlemper, L. L. Folgoc, et al., “Attention U-Net: Learning Where to Look for the Pancreas,”arXiv preprint arXiv:1804.03999, 2018

work page internal anchor Pith review arXiv 2018
[16]

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,

E. Xie, W. Wang, Z. Yu, et al., “SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,” inAdvances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 12077–12090, 2021

2021
[17]

Allan, M., Shvets, A., Kurmann, T., Zhang, Z., Duggal, R., Su, Y. H., ... & Stoyanov, D. (2019). 2017 Robotic Instrument Segmentation Challenge.arXiv preprint arXiv:1902.06426

work page arXiv 2019
[18]

3D U-Net: learning dense volumetric segmentation from sparse annotation

C ¸ i¸ cek,¨Ozg¨ un, Abdulkadir, Ahmed, Lienkamp, Soeren S., Brox, Thomas, and Ronneberger, Olaf. 3D U-Net: learning dense volumetric segmentation from sparse annotation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424–432. Springer, 2016. 7 5 Appendix 5.1 Detailed results for UNet The model achieved its b...

2016