Learning Probabilistic Fusion of Multilabel Lesion Contours

Gal Cohen, Hayit Greenspan, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings, primarily in proximity to lesion contours. In this study we address the case where the experts opinion for those ambiguous areas is considered as a distribution over the possible values. We propose a novel method that modifies the experts' distributional opinion at ambiguous areas by fusing their markings based on their sensitivity and specificity. The algorithm can be applied at the end of any label fusion algorithm that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. The method was evaluated on the MICCAI 2016 challenge dataset, and outperformed previous methods.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538693308
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City


FundersFunder number
Ministry of Science and Technology, Israel


    • multiple annotators
    • multiple sclerosis
    • segmentation
    • soft labels


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