What question did this study set out to answer?

To examine the challenges and solutions in brain tumor segmentation using MRI data, focusing on multimodal and multidimensional approaches.

April 18, 2026

Brain Tumor Segmentation in MRI: A review on Multimodal missing segmentation and Multidimensional segmentation Approaches

Key Points

To examine the challenges and solutions in brain tumor segmentation using MRI data, focusing on multimodal and multidimensional approaches.
Reviewed technical challenges of missing multimodal imaging data.
Analyzed the use of 2D and 3D U-Net architectures for segmentation.
Summarized methodological advances in brain tumor imaging.
Identified key solutions for handling missing data in multimodal MRI.
Evaluated the effectiveness of 2D and 3D U-Net approaches in segmentation tasks.
Provided insights into clinical applications of segmentation methodologies.

Abstract

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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Cite This Study

Zhang et al. (Fri,) studied this question.

synapsesocial.com/papers/69e3207940886becb653f8ea https://doi.org/https://doi.org/10.1142/s0218001426370012

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