标题： 使用深度学习实例分割识别腹腔镜手术器械 显示英文标题

标题： Identifying Surgical Instruments in Laparoscopy Using Deep Learning Instance Segmentation

摘要： 记录的手术视频已成为医学内窥镜领域越来越重要的信息来源，因为记录的视频展示了手术的每一个细节。 然而，尽管视频录制如今变得很简单，但自动内容索引——医疗视频档案中基于内容的搜索的基础——由于视频内容的特殊性，仍然是一个巨大的挑战。 在本工作中，我们研究了从腹腔镜妇科手术视频中分割和识别手术器械。 更准确地说，我们评估了使用基于区域的全卷积网络进行实例感知（1）器械分割以及（2）器械识别的可实现性能。 虽然第一部分仅涉及实例的二值分割（即区分器械或背景），我们也研究了多类器械识别（即识别器械的类型）。 我们的评估结果表明，即使训练样本数量适中，我们也能以相当高的准确性定位和分割器械区域。 然而，结果也表明，确定特定器械仍然非常具有挑战性，这是由于手术器械本身的高度相似性。 显示英文摘要

摘要： Recorded videos from surgeries have become an increasingly important information source for the field of medical endoscopy, since the recorded footage shows every single detail of the surgery. However, while video recording is straightforward these days, automatic content indexing - the basis for content-based search in a medical video archive - is still a great challenge due to the very special video content. In this work, we investigate segmentation and recognition of surgical instruments in videos recorded from laparoscopic gynecology. More precisely, we evaluate the achievable performance of segmenting surgical instruments from their background by using a region-based fully convolutional network for instance-aware (1) instrument segmentation as well as (2) instrument recognition. While the first part addresses only binary segmentation of instances (i.e., distinguishing between instrument or background) we also investigate multi-class instrument recognition (i.e., identifying the type of instrument). Our evaluation results show that even with a moderately low number of training examples, we are able to localize and segment instrument regions with a pretty high accuracy. However, the results also reveal that determining the particular instrument is still very challenging, due to the inherently high similarity of surgical instruments.