Review highlights advancements in segmenting brain tumors using multimodal data and U-Net architectures.
Brain tumor segmentation aims to accurately identify and delineate tumor regions from brain imaging modalities, such as Magnetic Resonance Imaging (MRI). This review focuses on two central topics in the field: (1) the technical challenges and solutions associated with missing multimodal imaging data, and (2) the development and application of 2D and 3D U-Net architectures along with their variants for brain tumor segmentation. By systematically summarizing key methodological advances, this article provides a comprehensive reference for both research and clinical practice in brain tumor analysis.
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Zhang et al. (2026) studied this question.
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