Article: An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2021 Vol.14 No.4 pp.315 - 332 Abstract: According to the World Alzheimer Report 2019, over 50 million people are living with dementia globally and it will increase to 152 million by 2050. Alzheimer's disease (AD) is a common type of dementia. One of the methods for Alzheimer's disease detection is using medical imaging especially using magnetic resonance imaging (MRI) in the brain concerning the white matter (WM), grey matter (GM), and cerebrospinal fluid regions (CSF). There are many methods based on a 3D CNN model for tissue segmentation but the disadvantage of the 3D model compared with the 2D model is the number of training objects will be decreased and the computation cost is increased. In this research, we proposed an effective approach to segment automatically WM, GM, and CSF in 3D brain MRI by using 2D and 3D CNN model combination. Our proposed method was evaluated with IBSR 18 and compared against state-of-the-art methods. The accuracy is 0.93, 0.92 and 0.79 for WM, GM, and CSF segmentation. Inderscience Publishers - linking academia, business and industry through research

Title: An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI

Authors: Pham The Bao; Tuan Tran; Tran Anh Tuan; Le Nhi Lam Thuy; Jin Young Kim

Addresses: Computer Science Department, Information Science Faculty, Sai Gon University, 273 An Duong Vuong Street, Ward 5, Ho Chi Minh City, Vietnam ' Faculty of Maths and Computer Science, University of Science, Vietnam National University, Ho Chi Minh, 227 Nguyen Van Cu Street, Ward 5, Ho Chi Minh City, Vietnam ' Faculty of Maths and Computer Science, University of Science, Vietnam National University, Ho Chi Minh, 227 Nguyen Van Cu Street, Ward 5, Ho Chi Minh City, Vietnam ' Information Systems Department, Information Science Faculty, Sai Gon University, 273 An Duong Vuong Street, Ward 5, Ho Chi Minh City, Vietnam ' Department of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, South Korea

Abstract: According to the World Alzheimer Report 2019, over 50 million people are living with dementia globally and it will increase to 152 million by 2050. Alzheimer's disease (AD) is a common type of dementia. One of the methods for Alzheimer's disease detection is using medical imaging especially using magnetic resonance imaging (MRI) in the brain concerning the white matter (WM), grey matter (GM), and cerebrospinal fluid regions (CSF). There are many methods based on a 3D CNN model for tissue segmentation but the disadvantage of the 3D model compared with the 2D model is the number of training objects will be decreased and the computation cost is increased. In this research, we proposed an effective approach to segment automatically WM, GM, and CSF in 3D brain MRI by using 2D and 3D CNN model combination. Our proposed method was evaluated with IBSR 18 and compared against state-of-the-art methods. The accuracy is 0.93, 0.92 and 0.79 for WM, GM, and CSF segmentation.

Keywords: medical imaging; image segmentation; brain MRI segmentation; tiny 3D U-Net; 2D U-Net.

DOI: 10.1504/IJIIDS.2021.118565

International Journal of Intelligent Information and Database Systems, 2021 Vol.14 No.4, pp.315 - 332

Received: 25 Aug 2020
Accepted: 01 Feb 2021
Published online: 28 Oct 2021 *

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Title: An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI

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