Developing a loss function with TransUnet for brain tumor segmentation from MRI images
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https://doi.org/10.54939/1859-1043.j.mst.78.2022.28-38Keywords:
Deep neural networks; TransUnet; MRI Brain tumor segmentation; Tversky loss.Abstract
Segmentation of brain tumor in magnetic resonance images plays an important role in diagnosis and treatment planning for patients. However, brain tumor segmentation is a nontrivial task of the variations and differences in tumor sizes, topology, shapes, and the presence of intensity inhomogeneity. In this study, we proposed a new approach for brain tumor segmentation based on advances in deep neural networks. In particular, we propose using the TransUnet, a newly developed architecture based on Transformers and U-Net. In addition, we propose a new loss function to handle the size and shape variations of tumors. The approach is validated on the Brain LGG Segmentation. Experiments show performances of the proposed approach in comparison with other states of the arts.
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