Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.

基于图像的医疗器械跟踪是外科数据科学应用的一个组成部分。先前的研究已经解决了基于腹腔镜视频数据的检测、分割和跟踪医疗器械的任务。然而，所提出的方法在应用于具有挑战性的图像时仍然容易失败，并且不能很好地推广到未经训练的数据。本文介绍了海德堡结直肠 (HeiCo) 数据集 - 第一个公开可用的数据集，可对医疗器械检测和分割算法进行全面的基准测试，特别强调方法的稳健性和泛化能力。我们的数据集包括 30 个腹腔镜视频以及来自手术室医疗设备的三种不同类型腹腔镜手术的相应传感器数据。注释包括所有视频帧的手术阶段标签以及有关器械存在的信息以及 10,000 多个单独帧中手术器械（如果有）的相应实例分割掩模。这些数据已成功用于组织 2017 年和 2019 年内窥镜视觉挑战赛的国际比赛。