Binary Segmentation of Whole Tumor from Multimodal Brain MRI Using an Attention-Enhanced U-Net

Author(s)	Dr. Rajshree
Country	India
Abstract	Reliable segmentation of brain tumors from magnetic resonance imaging (MRI) is essential for clinical decision-making, radiotherapy planning, and disease progression analysis. Among the different tumor sub-regions, Whole Tumor (WT), which comprises enhancing tumor, tumor core, and peritumoral edema, offers clinically meaningful information while being comparatively robust to annotation variability. This work proposes an attention-enhanced U-Net architecture for binary WT segmentation using multimodal MRI data. The network integrates four MRI modalities—T1, T1ce, T2, and FLAIR—and employs attention gates within skip connections to selectively highlight tumor-relevant features and suppress background responses. Experimental evaluation on the BraTS dataset demonstrates improved Dice similarity, IoU, and sensitivity compared to baseline models.
Keywords	Brain tumor segmentation, Whole tumor, Multimodal MRI, Attention U-Net, Medical image analysis
Field	Computer
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-12

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International Journal on Science and Technology