Magnetic resonance imaging-based brain tissue segmentation using M-Net and deep embedded clustering for computer-aided Alzheimer’s disease analysis
Image segmentation divides an image into distinct regions. Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) plays an important role in the analysis of various neurological disorders. Alzheimer’s disease, the most common cause of dementia, is characterized by progressive cognitive and behavioral decline. In recent years, machine learning techniques have been increasingly applied to MRI analysis to support computer-aided neurological assessment. Most existing approaches rely on supervised learning, which requires large amounts of manually labeled MRI data. However, acquiring accurate annotations is both time-consuming and labor-intensive. In contrast, unsupervised image segmentation techniques can automatically identify meaningful tissue structures without requiring labeled training data. In this study, an unsupervised brain MRI segmentation framework based on M-Net and deep embedded clustering (DEC) is proposed. The framework integrates deep feature learning with clustering to achieve accurate segmentation of white matter and gray matter regions. The proposed M-Net-based DEC framework achieved an overall segmentation accuracy of 96.12% on the Internet Brain Segmentation Repository 2.0 dataset.
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