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

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

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 da...

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Bibliographic Details
Main Authors: Faizaan Fazal Khan, Jun-Hyung Kim, Chun-Su Park, Ji-In Kim, Goo-Rak Kwon
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Alzheimer’s disease
3D contrast limited adaptive histogram equalization (CLAHE)
EADC-ADNI Harmonized protocol (HarP) dataset
hippocampus segmentation
selective coefficient-enhanced 3D wavelet transform for MRI segmentation (SCE-3DWT)
3D U-Net
Online Access:https://ieeexplore.ieee.org/document/11027081/
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https://ieeexplore.ieee.org/document/11027081/

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