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WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI

Barzegar, Z ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.bspc.2021.102617
  3. Publisher: Elsevier Ltd , 2021
  4. Abstract:
  5. 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. © 2021 Elsevier Ltd
  6. Keywords:
  7. Diseases ; Graphic methods ; Image segmentation ; Learning algorithms ; Machine learning ; Magnetic resonance imaging ; Atlas-based segmentation ; Cancer detection ; Glioma brain tumor ; Label fusions ; Label propagation ; Learning frameworks ; Multi-modal brain MRI ; Patch-based segmentation ; Probabilistic graphical models ; Segmentation results ; Tumors ; Controlled study ; Diagnostic accuracy ; Feature extraction ; Glioma ; Human ; Image analysis ; Image processing ; Image segmentation ; Neuroimaging ; Nuclear magnetic resonance imaging ; Priority journal ; Semi supervised machine learning ; Weighted label fusion learning framework
  8. Source: Biomedical Signal Processing and Control ; Volume 68 , 2021 ; 17468094 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1746809421002147