Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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, individual frames.
The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges and The vision is to derive data science-based methodology to provide physicians with the right assistance at the right time.
One active field of research consists in analyzing laparoscopic video data to provide context-aware intraoperative assistance to the surgical team during minimally-invasive surgery. Accurate tracking of surgical instruments is a fundamental prerequisite for many assistance tasks ranging from surgical navigation 2 to skill analysis 3 and complication prediction. While encouraging results for detecting, segmenting and tracking medical devices in relatively controlled settings have been achieved 4 , the proposed methods still tend to fail when applied to challenging images e.
