USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging

Author:

Ostmeier SophieORCID,Axelrod Brian,Isensee FabianORCID,Bertels JeroenORCID,Mlynash MichaelORCID,Christensen SorenORCID,Lansberg Maarten G.,Albers Gregory W.,Sheth Rajen,Verhaaren Benjamin F.J.ORCID,Mahammedi AbdelkaderORCID,Li Li-JiaORCID,Zaharchuk GregORCID,Heit Jeremy J.ORCID

Funder

Deutsche Forschungsgemeinschaft

California Department of Fish and Game

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference42 articles.

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4. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features;Bakas;Sci. Data,2017

5. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge;Bakas,2018

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