2020 CATARACTS Semantic Segmentation Challenge.
Imanol LuengoMaria Grammatikopoulour.Ali Sayadani Nader MohammadiChris WalshChinedu Innocent NwoyeDeepak AlapattNicolas PadoyZhen-Liang NiChen-Chen FanGui‐Bin BianZeng‐Guang HouHeonjin HaJiacheng WangHaojie WangDong GuoLu WangGuotai WangMobarakol IslamBharat GiddwaniHongliang RenTheodoros PissasClaudio S. RavasioMartin HuberJeremy BirchJoan M. Nunez do RioLyndon da CruzChristos BergelesHongyu ChenFucang JiaNikhil Kumar TomarDebesh JhaMichael RieglerPål HalvorsenSophia BanoUddhav VaghelaJianyuan HongHaili YeFeihong HuangDa‐Han WangDanail Stoyanov
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Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set.Cite
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Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this paper investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. Seventy six medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: 1) labeling of images showing tissue regions and 2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.
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