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arXiv · 2501.00525

Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis

Cheng Yuan·Jian Jiang·Kunyi Yang·Lv Wu·Rui Wang·Zi Meng·Haonan Ping·Ziyu Xu·Yifan Zhou·Wanli Song·Hesheng Wang·Yueming Jin·Qi Dou·Yutong Ban
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Abstract

Surgical video segmentation is critical for AI to interpret spatial-temporal dynamics in surgery, yet model performance is constrained by limited annotated data. The SAM2 model, pretrained on natural videos, offers potential for zero-shot surgical segmentation, but its applicability in complex surgical environments, with challenges like tissue deformation and instrument variability, remains unexplored. We present the first comprehensive evaluation of the zero-shot capability of SAM2 in 9 surgical datasets (17 surgery types), covering laparoscopic, endoscopic, and robotic procedures. We analyze various prompting (points, boxes, mask) and {finetuning (dense, sparse) strategies}, robustness to surgical challenges, and generalization across procedures and anatomies. Key findings reveal that while SAM2 demonstrates notable zero-shot adaptability in structured scenarios (e.g., instrument segmentation, {multi-organ segmentation}, and scene segmentation), its performance varies under dynamic surgical conditions, highlighting gaps in handling temporal coherence and domain-specific artifacts. These results highlight future pathways to adaptive data-efficient solutions for the surgical data science field.

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Cheng Yuan, Jian Jiang, Kunyi Yang, Lv Wu, Rui Wang, Zi Meng, Haonan Ping, Ziyu Xu, Yifan Zhou, Wanli Song, Hesheng Wang, Yueming Jin, Qi Dou, Yutong Ban. 2024-12-31. Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis. https://arxiv.org/abs/2501.00525

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