Title: Medical image registration and automatic hippocampus segmentation through convolutional neural network

Authors: S. Durga Prasad; K.S.N. Murthy; B. Kannan; C. Sivakumaran

Addresses: Department of Computer Science and Engineering, Baba Institute of Technology and Sciences, Visakhapatnam, India ' Department of Computer Science and Engineering, Baba Institute of Technology and Sciences, Visakhapatnam, India ' Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, India ' Machine Learning Engineer, Photon Technologies, Chennai, India

Abstract: Alzheimer's disease (AD) is a brain degenerative ailment that progresses and is irreversible. Mild cognitive impairment, known as MCI, is a clinical indicator that AD may eventually develop. In order to effectively treat and prevent AD, an accurate diagnosis of the illness's early stages is required. AD often manifests its symptoms first in the hippocampus. Deep machine learning was used in this project with the intention of achieving its aim of segmenting a specific region. The suggested method's performance was compared to manual segmentation using similarity measures. The performance of a CNN that segments the hippocampus directly is inferior to that of any contouring technique and the findings were 96% accurate. The quantitative results are improved by the application of stringent corrections to the data, although the gap is still rather large. The suggested technique is promising and may be expanded in AD prognosis by predicting hippocampal volume changes in the early stages of the illness.

Keywords: Alzheimer's disease; hippocampus; magnetic resonance imaging; convolutional neural network images; TBI; U-net.

DOI: 10.1504/IJMEI.2025.149548

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.6, pp.537 - 546

Received: 16 Jun 2022
Accepted: 10 Sep 2022
Published online: 07 Nov 2025 *

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