提升机器人微创手术中细长器械的分割精度
Microsurgical Instrument Segmentation for Robot-Assisted Surgery
- 融合亮度通道+跳跃注意力，保留细长结构特征
- 迭代反馈模块使重叠器械分割连续性提升5.37%
- 专用于微创手术的精细标注数据集可公开获取
精准分割细小结构对微创手术场景理解至关重要，但受限于分辨率损失、低对比度和类别不平衡问题。本文提出MISRA框架，通过在RGB输入中加入亮度通道，集成跳跃注意力以保留细长特征，并引入迭代反馈模块（IFM）实现多轮迭代中的连续性恢复。此外，我们构建了一个专用微手术数据集，包含细粒度的手术器械标注，涵盖细长物体，为鲁棒评估提供基准。数据集可在https://huggingface.co/datasets/KIST-HARILAB/MISAW-Seg获取。实验表明，MISRA在同类方法中表现优异，平均类别交并比（mIoU）提升5.37%，在器械接触与重叠区域预测更稳定。该成果为计算机辅助及机器人微创手术的可靠场景解析提供了有力支持。
原文摘要 · Abstract (English)
Accurate segmentation of thin structures is critical for microsurgical scene understanding but remains challenging due to resolution loss, low contrast, and class imbalance. We propose Microsurgery Instrument Segmentation for Robotic Assistance(MISRA), a segmentation framework that augments RGB input with luminance channels, integrates skip attention to preserve elongated features, and employs an Iterative Feedback Module(IFM) for continuity restoration across multiple passes. In addition, we introduce a dedicated microsurgical dataset with fine-grained annotations of surgical instruments including thin objects, providing a benchmark for robust evaluation Dataset available at https://huggingface.co/datasets/KIST-HARILAB/MISAW-Seg. Experiments demonstrate that MISRA achieves competitive performance, improving the mean class IoU by 5.37% over competing methods, while delivering more stable predictions at instrument contacts and overlaps. These results position MISRA as a promising step toward reliable scene parsing for computer-assisted and robotic microsurgery.
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