Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts’ opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts’ markings fusion 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. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings|
|Editors||Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||8|
|State||Published - 2019|
|Event||22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China|
Duration: 13 Oct 2019 → 17 Oct 2019
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019|
|Period||13/10/19 → 17/10/19|
Bibliographical notePublisher Copyright:
© 2019, Springer Nature Switzerland AG.
- MS lesion segmentation
- STAPLE algorithm
- Soft labels