Abstract
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 this https URL. 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.
Abstract (translated)
精确分割微细结构对于显微手术场景的理解至关重要，但由于分辨率损失、对比度低和类别不平衡等问题，这一任务仍然具有挑战性。我们提出了一个名为“机器人辅助显微外科器械分割”（Microsurgery Instrument Segmentation for Robotic Assistance, MISRA）的分割框架。该框架通过增加RGB输入中的亮度通道来增强图像信息，并集成跳跃注意力机制以保留拉长特征，同时采用迭代反馈模块(IFM)在多次传递中恢复连续性。此外，我们还引入了一个专门针对显微手术的数据集，其中包含了对手术器械（包括细小物体）的精细标注，为稳健评估提供了基准数据集。该数据集可在提供的网址获取。 实验结果表明，MISRA实现了与竞争方法相媲美的性能，在平均类别交并比(mIoU)上提高了5.37%，并且在器械接触和重叠时能提供更稳定的预测。这些成果将MISRA定位为向计算机辅助和机器人显微手术中可靠场景解析迈出的有希望的一步。
URL
https://arxiv.org/abs/2509.11727
