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

• URL: http://arxiv.org/abs/2005.03501v5
• Date: Tue, 23 Feb 2021 14:32:49 GMT
• Title: Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
• Authors: Lena Maier-Hein, Martin Wagner, Tobias Ross, Annika Reinke, Sebastian Bodenstedt, Peter M. Full, Hellena Hempe, Diana Mindroc-Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Anna Kisilenko, Benjamin M\"uller, Tornike Davitashvili, Manuela Capek, Minu Tizabi, Matthias Eisenmann, Tim J. Adler, Janek Gr\"ohl, Melanie Schellenberg, Silvia Seidlitz, T. Y. Emmy Lai, B\"unyamin Pekdemir, Veith Roethlingshoefer, Fabian Both, Sebastian Bittel, Marc Mengler, Lars M\"undermann, Martin Apitz, Annette Kopp-Schneider, Stefanie Speidel, Hannes G. Kenngott, Beat P. M\"uller-Stich
• Abstract summary: 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. 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.
• Score: 1.6276355161958829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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