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000168373 1001_ $$0P:(DE-He78)26a1176cd8450660333a012075050072$$aMaier-Hein, Lena$$b0$$eFirst author
000168373 245__ $$aHeidelberg colorectal data set for surgical data science in the sensor operating room.
000168373 260__ $$aLondon$$bNature Publ. Group$$c2021
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000168373 520__ $$aImage-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.
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000168373 7001_ $$aWagner, Martin$$b1
000168373 7001_ $$0P:(DE-He78)47f4a97043307540977baf09618b5d3d$$aRoss, Tobias$$b2
000168373 7001_ $$0P:(DE-He78)97e904f47dab556a77c0149cd0002591$$aReinke, Annika$$b3
000168373 7001_ $$0P:(DE-He78)7e1dc3bb70d3108f5a58a20d7fc75981$$aBodenstedt, Sebastian$$b4
000168373 7001_ $$0P:(DE-He78)e9dc924f238fa6cc29465942875fe8f0$$aFull, Peter M$$b5$$udkfz
000168373 7001_ $$0P:(DE-He78)da8f1598d3f8fda8a28b67a3b2d740f3$$aHempe, Hellena$$b6
000168373 7001_ $$0P:(DE-He78)7fd32778819c90575f8df07034e04808$$aMindroc-Filimon, Diana$$b7
000168373 7001_ $$0P:(DE-He78)77a2a5b07dcbd46277a18a32372ea154$$aScholz, Patrick$$b8
000168373 7001_ $$0P:(DE-He78)96509db5798da9bcccf0d34db39f50e7$$aTran, Thuy Nuong$$b9
000168373 7001_ $$0P:(DE-He78)861d46b75ffd6c1abb386ca3c5197bac$$aBruno, Pierangela$$b10
000168373 7001_ $$aKisilenko, Anna$$b11
000168373 7001_ $$aMüller, Benjamin$$b12
000168373 7001_ $$aDavitashvili, Tornike$$b13
000168373 7001_ $$aCapek, Manuela$$b14
000168373 7001_ $$0P:(DE-He78)26651d9aa10255ad4f35610a56aa91e8$$aTizabi, Minu D$$b15
000168373 7001_ $$0P:(DE-He78)c9d6245b17f0ab26eeed345cb00d3359$$aEisenmann, Matthias$$b16
000168373 7001_ $$0P:(DE-He78)ae131915396ed2f27752c043e123897e$$aAdler, Tim J$$b17
000168373 7001_ $$0P:(DE-He78)fd657bfbb3c4757ac029bb6b56ab9b71$$aGröhl, Janek$$b18
000168373 7001_ $$0P:(DE-He78)9d0e93f03c73f265ef93b2217b023d60$$aSchellenberg, Melanie$$b19
000168373 7001_ $$0P:(DE-He78)6f627fc52580baaa9c8dd007c7b32f8f$$aSeidlitz, Silvia$$b20
000168373 7001_ $$aLai, T Y Emmy$$b21
000168373 7001_ $$0P:(DE-He78)01006b3b56865f6bdad60eb489028403$$aPekdemir, Bünyamin$$b22
000168373 7001_ $$aRoethlingshoefer, Veith$$b23
000168373 7001_ $$aBoth, Fabian$$b24
000168373 7001_ $$aBittel, Sebastian$$b25
000168373 7001_ $$aMengler, Marc$$b26
000168373 7001_ $$aMündermann, Lars$$b27
000168373 7001_ $$aApitz, Martin$$b28
000168373 7001_ $$0P:(DE-He78)bb6a7a70f976eb8df1769944bf913596$$aKopp-Schneider, Annette$$b29
000168373 7001_ $$aSpeidel, Stefanie$$b30
000168373 7001_ $$00000-0001-6066-8238$$aNickel, Felix$$b31
000168373 7001_ $$00000-0002-0895-4015$$aProbst, Pascal$$b32
000168373 7001_ $$aKenngott, Hannes G$$b33
000168373 7001_ $$aMüller-Stich, Beat P$$b34
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