WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI. (July 2021) - Barzegar, Zeynab; Jamzad, Mansour
Highlights: A semi-supervised graph-based label propagation algorithm for glioma segmentation. Formulate the problem as an information propagation through a probabilistic graph. Label information is propagated from higher to lower confidence vertices. Both atlases and target images contribute to the final segmentation results. Final labels are obtained by label fusion of the propagated label probabilities. Abstract: Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a probabilistic graph based method. It combines the advantages of label propagation and patch-based segmentation on a parametric graph. To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low and high-grade glioma tumors. The experimental results show that the proposed framework has accurate segmentation results. Compared with the state-of-the-art methods, the proposed framework could obtain the best dice score for segmenting the "whole tumor" (WT) and "tumor core" (TC) regions. The segmentation result of the "enhancing active tumor" (ET) region is similar to the most recent works compared in this paper.