@article {27638,
	title = {Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge},
	journal = {Medical Image Analysis},
	year = {2020},
	month = {11/2020},
	publisher = {Elsevier},
	abstract = {Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting,\&nbsp; segmenting and tracking of medical instruments based on endoscopic video im-ages have been proposed in the literature, key limitations remain to be addressed:\&nbsp; Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts).\&nbsp; Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In\&nbsp;an\&nbsp;effort\&nbsp;to\&nbsp;promote\&nbsp;solutions\&nbsp;for\&nbsp;these\&nbsp;limitations,\&nbsp; we\&nbsp;organized\&nbsp;the Robust\&nbsp; Medical\&nbsp; Instrument\&nbsp; Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms.\&nbsp; For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation.\&nbsp; The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).\&nbsp;},
	author = {Ro{\ss}, Tobias and Reinke, Annika and Peter M. {Full} and Wagner, Martin and Kenngott, Hannes and Apitz, Martin and Hempe, Hellena and Diana Mindroc {Filimon} and Scholz, Patrick and Thuy Nuong {Tran} and Bruno, Pierangela and Pablo Arbel ́ {aez} and Bin Bian, Gui- and Bodenstedt, Sebastian and Jon Lindstrom {Bolmgren} and Laura Bravo-S ́ {anchez} and Bin Chen, Hua- and Cristina Gonz ́ {alez} and Guo, Dong and Halvorsen, P{\r a}l and Heng, Pheng-Ann and Hosgor, Enes and Hou, Zeng-Guang and Isensee, Fabian and Jha, Debesh and Jiang, Tingting and Jin, Yueming and Kirtac, Kadir and Kletz, Sabrina and Leger, Stefan and Li, Zhixuan and Klaus H. {Maier-Hein} and Ni, Zhen-Liang and Riegler, Michael and Schoeffmann, Klaus and Shi, Ruohua and Speidel, Stefanie and Stenzel, Michael and Twick, Isabell and Wang, Gutai and Wang, iacheng and Wang, Liansheng and Wang, Lu and Zhang, Yujie and Zhou, Yan-Jie and Zhu, Lei and Wiesenfarth, Manuel and Kopp-Schneider, Annette and Beat P. M ̈ {uller-Stich} and Maier-Hein, Lena}
}
