Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy

Yihan Lang

doi:10.47297/wspbdsWSP2752-630545.20260601

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Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy

Volume 6, Issue 1

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Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy
Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy
Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
Vol. 6, Issue 1, Pages: 205-210(2026)
Published：30 March 2026
DOI：10.47297/wspbdsWSP2752-630545.20260601
Accepted：

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Lang Yihan.Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy[J].Biomedical Data Science,2026,06(01):205-210.
DOI：

Lang Yihan.Coverage-Driven Soft Labeling for Surgical Video Temporal Segmentation: Application to Radical Prostatectomy[J].Biomedical Data Science,2026,06(01):205-210. DOI： 10.47297/wspbdsWSP2752-630545.20260601.

摘要

Abstract

Radical prostatectomy

the primary curative treatment for prostate cancer

presents significant challenges due to its complex workﬂow and technical demands. Recent advances in robotic-assisted surgery and deep learning have enabled intelligent surgical assistance systems. This paper proposes a novel temporal segmentation framework combining VideoMAE feature extraction with MS-TCN++ temporal modeling

enhanced by a coverage-driven soft-label strategy for improved frame-wise classiﬁcation in prostatectomy videos. Our method effectively addresses key challenges including background dominance

boundary ambiguity

and temporal inconsistencies. Experimental validation demonstrates superior performance in both accuracy and robustness

offering promising potential for real-time surgical monitoring and safety assistance.

关键词

Keywords

references

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