1 research outputs found
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand
Gliomas are brain tumors composed of different highly heterogeneous
histological subregions. Image analysis techniques to identify relevant tumor
substructures have high potential for improving patient diagnosis, treatment
and prognosis. However, due to the high heterogeneity of gliomas, the
segmentation task is currently a major challenge in the field of medical image
analysis. In the present work, the database of the Brain Tumor Segmentation
(BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was
studied. A segmentation methodology based on the design and application of
convolutional neural networks (CNNs) combined with original post-processing
techniques with low computational demand was proposed. The post-processing
techniques were the main responsible for the results obtained in the
segmentations. The segmented regions were the whole tumor, the tumor core, and
the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934,
0.8376, and 0.8113, respectively. These results reached the state of the art in
glioma segmentation determined by the winners of the challenge.Comment: 34 pages, 12 tables, 23 figure