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Metrics Matter in Surgical Phase Recognition

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arxiv 2305.13961 v1 pith:SS4ZDLBC submitted 2023-05-23 cs.CV

Isabel Funke , Dominik Rivoir , Stefanie Speidel This is my paper · ORCID
classification cs.CV
keywords evaluationphaserecognitionsurgicaldetailsresultscholec80differences
verification ladder T0 review T1 audit T2 compute T3 formal
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Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to differences in the evaluation process and incomplete reporting of evaluation details. In particular, the details of metric computation can vary widely between different studies. To raise awareness of potential inconsistencies, this paper summarizes common deviations in the evaluation of phase recognition algorithms on the Cholec80 benchmark. In addition, a structured overview of previously reported evaluation results on Cholec80 is provided, taking known differences in evaluation protocols into account. Greater attention to evaluation details could help achieve more consistent and comparable results on the surgical phase recognition task, leading to more reliable conclusions about advancements in the field and, finally, translation into clinical practice.

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Cited by 5 Pith papers

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

  1. Current validation practice undermines surgical AI development

    q-bio.OT 2025-11 conditional novelty 7.0 of 10

    A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.

  2. HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognition

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hierarchical text-guided network that constructs and calibrates phase segments improves surgical phase recognition, setting a high Jaccard on Cholec80 and reporting large gains on a private LCRS-100 benchmark.

  3. CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery

    cs.CV 2025-06 conditional novelty 6.0 of 10

    CAT-SG is a new cataract surgery scene graph dataset with 1.811 million relation annotations, a two-class technique recognition task, and a query-based scene graph generation baseline.

  4. SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A teacher-student video transformer with temporal consistency pseudo-labeling and prototype contrastive learning matches full-supervision surgical phase recognition using only 1/4 of Cholec80 labels.

  5. Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation

    cs.CV 2025-01 reject novelty 5.0 of 10

    Kinematics Adaptive Frame Recognition selects frames with significant tool motion, reducing training data 5x to 10x and modestly improving phase segmentation accuracy over uniform sampling.

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