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unsupervised domain-adaptive semantic segmentation for Surgical Instruments Leveraging Dropout-Enhanced Dual Heads and Coarse-Grained Classification Branch

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年

作者： Li, Ziqian Wang, Zhengyu Xu, Xinzhou Chen, Yongfa Schuller, Bjorn W. Hefei University of Technology School of Mechanical Engineering Hefei China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China Graz University of Technology Signal Processing and Speech Communication Laboratory Graz Austria Chair of Health Informatics Munich Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany Imperial College London GLAM – the Group on Language Audio and Music London United Kingdom

Accurate semantic segmentation for surgical instruments is crucial in robot-assisted minimally invasive surgery, mainly regarded as a core module in surgical-instrument tracking and operation guidance. Nevertheless, it is usually difficult for existing semantic surgical-instrument segmentation approaches to adapt to unknown surgical scenes, particularly due to their insufficient consideration for reducing the domain gaps across different scenes. To address this issue, we propose an unsupervised domain-adaptive semantic segmentation approach for surgical instruments, leveraging Dropout-enhanced Dual Heads and Coarse-Grained classification branch (D2HCG). The proposed approach comprises dropout-enhanced dual heads for diverse feature representation, and a coarse-grained classification branch for capturing complexities across varying granularities. This incorporates consistency loss functions targeting fine-grained features and coarse-grained granularities, aiming to reduce crossscene domain gaps. Afterwards, we perform experiments in crossscene surgical-instrument semantic segmentation cases, with the experimental results reporting the effectiveness for the proposed approach, compared with state-of-the-art semantic segmentation ones. © 2018 IEEE.

关键词： coarse-grained classification dropout enhancement Surgical instruments unsupervised domain-adaptive semantic segmentation

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