论文标题
使用注意手术将医疗器械细分为微创手术
Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask
论文作者
论文摘要
精确定位和细分医疗工具在微创手术的图像中，医疗仪器分割是医疗图像处理中多个任务的重要第一步。但是，图像降解，小型仪器以及不同手术类型之间的概括使医疗仪器分割具有挑战性。为了应对这些挑战，我们适应了对象提案生成系统的注意策略，并提出了专门的后处理，以选择有前途的建议。 2019年强大的MIS挑战赛的结果表明，我们适应的注意力掩护系统是产生最先进的性能的坚实基础。我们在对象提案生成框架中的评估表明，我们适应的注意力掩护系统可用于图像降解，很好地概括为看不见的手术类型，并且可以很好地应对小型乐器。
Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical instrument segmentation challenging. To cope with these challenges, we adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a strong foundation for generating state-of-the-art performance. Our evaluation in an object proposal generation framework shows that our adapted AttentionMask system is robust to image degradations, generalizes well to unseen types of surgeries, and copes well with small instruments.
