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The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark

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arxiv 2312.12429 v3 pith:OKODJT2I submitted 2023-12-19 cs.CV

The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark

Aditya Murali , Deepak Alapatt , Pietro Mascagni , Armine Vardazaryan , Alain Garcia , Nariaki Okamoto , Guido Costamagna , Didier Mutter
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Jacques Marescaux Bernard Dallemagne Nicolas Padoy
This is my paper
classification cs.CV
keywords datasetannotatedendoscapesframessegmentationvideosassessmentanatomy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS). Endoscapes comprises 201 LC videos with frames annotated sparsely but regularly with segmentation masks, bounding boxes, and CVS assessment by three different clinical experts. Altogether, there are 11090 frames annotated with CVS and 1933 frames annotated with tool and anatomy bounding boxes from the 201 videos, as well as an additional 422 frames from 50 of the 201 videos annotated with tool and anatomy segmentation masks. In this report, we provide detailed dataset statistics (size, class distribution, dataset splits, etc.) and a comprehensive performance benchmark for instance segmentation, object detection, and CVS prediction. The dataset and model checkpoints are publically available at https://github.com/CAMMA-public/Endoscapes.

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Cited by 5 Pith papers

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

  1. Current validation practice undermines surgical AI development

    q-bio.OT 2025-11 accept novelty 7.0

    A consensus-based catalog of 18 validation pitfalls, with evidence that common practices understate uncertainty, hide failures, and flip algorithm rankings in surgical video AI.

  2. RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering

    cs.CV 2026-05 unverdicted novelty 6.0

    RoboSurg-VQA is a new segmentation-aware VQA benchmark created by repurposing public surgical datasets with fixed clinically motivated questions and closed answer sets.

  3. Current validation practice undermines surgical AI development

    q-bio.OT 2025-11 conditional novelty 6.0

    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.

  4. Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models

    cs.CV 2026-05 unverdicted novelty 5.0

    DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmar...

  5. SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking

    cs.CV 2025-11 conditional novelty 5.0

    SAM2S, a SAM2 variant trained on the new 61k-frame SA-SV surgical benchmark, improves average J&F to 80.42 at 68 FPS for interactive surgical-video object segmentation.