Xiaoliang Lei1, Xiaosheng Yu2,*, Hao Wu3, Chengdong Wu2,*, Jingsi Zhang2
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069
Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >
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