用深度学习精准分割腹腔镜手术中的器械，助力医疗视频自动检索
Identifying Surgical Instruments in Laparoscopy Using Deep Learning Instance Segmentation
- 基于区域的全卷积网络实现器械实例分割与识别
- 少量训练数据下仍能实现高精度器械定位与分割
- 适合医疗影像分析、手术自动化研究者参考
手术录像已成为医学内窥镜领域的重要信息来源，记录了手术全过程的每一个细节。尽管视频录制已十分便捷，但自动内容索引——实现医疗视频库中基于内容的检索——仍面临巨大挑战，原因在于视频内容特殊。本文研究腹腔镜妇科手术视频中手术器械的分割与识别，评估基于区域的全卷积网络在实例感知下的性能，包括（1）器械与背景的二值分割，以及（2）多类器械类型识别。实验表明，即使训练样本数量有限，仍可实现较高的器械区域定位与分割准确率。然而，由于手术器械本身高度相似，精确识别具体器械类型仍具挑战性。
原文摘要 · Abstract (English)
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.
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