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Image-based tracking of medical instruments is an integral part of surgicaldata science applications. Previous research has addressed the tasks ofdetecting, segmenting and tracking medical instruments based on laparoscopicvideo data. However, the proposed methods still tend to fail when applied tochallenging images and do not generalize well to data they have not beentrained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set -the first publicly available data set enabling comprehensive benchmarking ofmedical instrument detection and segmentation algorithms with a specificemphasis on method robustness and generalization capabilities. Our data setcomprises 30 laparoscopic videos and corresponding sensor data from medicaldevices in the operating room for three different types of laparoscopicsurgery. Annotations include surgical phase labels for all video frames as wellas information on instrument presence and corresponding instance-wisesegmentation masks for surgical instruments (if any) in more than 10,000individual frames. The data has successfully been used to organizeinternational competitions within the Endoscopic Vision Challenges 2017 and2019.

Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room