Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).

Autor: Hazarika RA; Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, 793022, India. rahazarika@gmail.com., Maji AK; Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, 793022, India., Syiem R; Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, 793022, India., Sur SN; Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, 737136, India., Kandar D; Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, 793022, India. kdebdatta@gmail.com.
Jazyk: angličtina
Zdroj: Journal of digital imaging [J Digit Imaging] 2022 Aug; Vol. 35 (4), pp. 893-909. Date of Electronic Publication: 2022 Mar 18.
DOI: 10.1007/s10278-022-00613-y
Abstrakt: Hippocampus is a part of the limbic system in human brain that plays an important role in forming memories and dealing with intellectual abilities. In most of the neurological disorders related to dementia, such as, Alzheimer's disease, hippocampus is one of the earliest affected regions. Because there are no effective dementia drugs, an ambient assisted living approach may help to prevent or slow the progression of dementia. By segmenting and analyzing the size/shape of hippocampus, it may be possible to classify the early dementia stages. Because of complex structure, traditional image segmentation techniques can't segment hippocampus accurately. Machine learning (ML) is a well known tool in medical image processing that can predict and deliver the outcomes accurately by learning from it's previous results. Convolutional Neural Networks (CNN) is one of the most popular ML algorithms. In this work, a U-Net Convolutional Network based approach is used for hippocampus segmentation from 2D brain images. It is observed that, the original U-Net architecture can segment hippocampus with an average performance rate of 93.6%, which outperforms all other discussed state-of-arts. By using a filter size of [Formula: see text], the original U-Net architecture performs a sequence of convolutional processes. We tweaked the architecture further to extract more relevant features by replacing all [Formula: see text] kernels with three alternative kernels of sizes [Formula: see text], [Formula: see text], and [Formula: see text]. It is observed that, the modified architecture achieved an average performance rate of 96.5%, which outperforms the original U-Net model convincingly.
(© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
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