TY  - JOUR
AU  - Maier-Hein, Lena
AU  - Wagner, Martin
AU  - Ross, Tobias
AU  - Reinke, Annika
AU  - Bodenstedt, Sebastian
AU  - Full, Peter M
AU  - Hempe, Hellena
AU  - Mindroc-Filimon, Diana
AU  - Scholz, Patrick
AU  - Tran, Thuy Nuong
AU  - Bruno, Pierangela
AU  - Kisilenko, Anna
AU  - Müller, Benjamin
AU  - Davitashvili, Tornike
AU  - Capek, Manuela
AU  - Tizabi, Minu D
AU  - Eisenmann, Matthias
AU  - Adler, Tim J
AU  - Gröhl, Janek
AU  - Schellenberg, Melanie
AU  - Seidlitz, Silvia
AU  - Lai, T Y Emmy
AU  - Pekdemir, Bünyamin
AU  - Roethlingshoefer, Veith
AU  - Both, Fabian
AU  - Bittel, Sebastian
AU  - Mengler, Marc
AU  - Mündermann, Lars
AU  - Apitz, Martin
AU  - Kopp-Schneider, Annette
AU  - Speidel, Stefanie
AU  - Nickel, Felix
AU  - Probst, Pascal
AU  - Kenngott, Hannes G
AU  - Müller-Stich, Beat P
TI  - Heidelberg colorectal data set for surgical data science in the sensor operating room.
JO  - Scientific data
VL  - 8
IS  - 1
SN  - 2052-4463
CY  - London
PB  - Nature Publ. Group
M1  - DKFZ-2021-00862
SP  - 101
PY  - 2021
N1  - #EA:E130#
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:33846356
DO  - DOI:10.1038/s41597-021-00882-2
UR  - https://inrepo02.dkfz.de/record/168373
ER  -