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.
|Title of host publication||ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Apr 2020|
|Event||17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States|
Duration: 3 Apr 2020 → 7 Apr 2020
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||17th IEEE International Symposium on Biomedical Imaging, ISBI 2020|
|Period||3/04/20 → 7/04/20|
Bibliographical noteFunding Information:
This research was supported by the Ministry of Science & Technology, Israel
© 2020 IEEE.
- multiple annotators
- multiple sclerosis
- soft labels