A Resource-Efficient 3D U-Net for Hippocampus Segmentation Using CLAHE and SCE-3DWT Techniques

초록

Hippocampus segmentation on MRI (magnetic resonance imaging) plays a vital role in detecting, diagnosing, tracking, and monitoring neurodegenerative diseases, particularly Alzheimer’s disease. While larger datasets often provide an advantage in deep learning-based segmentation, smaller datasets pose unique challenges due to limited data variability and an increased risk of overfitting. This study addresses these challenges by developing a computationally efficient and accurate 3D U-Net model tailored for hippocampus segmentation. The proposed approach employs a preprocessing pipeline combining 3D Contrast Limited Adaptive Histogram Equalization (CLAHE) and Selective Coefficient-Enhanced 3D Wavelet Transform (SCE-3DWT), which enhances contrast and reduces noise for improved feature extraction. The experimental evaluation was conducted using the EADC-ADNI HarP dataset, comprising 135 hippocampal MRI scans with an input image size of 64 × 64 × 96 . The model achieved a Dice coefficient of 0.8838 and a Jaccard Index of 0.7920, surpassing recent state-of-the-art methods. Comparative analysis highlights reduced Over-Segmentation Ratio (OSR = FP/(FP+TP), 0.0594) and Under-Segmentation Ratio (USR = FN/(FN+TP), 0.0569, reflecting its robustness and generalization. The lightweight architecture, designed with a maximum filter size of 512, operates efficiently without relying on transfer learning, making it accessible for broader applications. Future work will focus on integrating post processing techniques, leveraging larger and more diverse datasets, and exploring higher-resolution volumetric data to further improve segmentation accuracy and clinical utility. This study contributes to the advancement of medical image analysis, offering a resource-efficient framework for precise hippocampus segmentation, with potential implications for improved Alzheimer’s disease management. © 2013 IEEE.

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