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321063

Automatic Multiple Sclerosis lesion segmentation using Patch-wise R-CNN

Article

Last updated: 05 Jan 2025

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Abstract

Multiple sclerosis (MS) could be considered one of the most severe neurological diseases, which can cause damage to the central nervous system. Because of the regular change in size, location and anatomical variation of MS lesions, it is a
challenge to accurately identify, characterize and quantify MS lesions on magnetic resonance imaging (MRI). Therefore, MS lesion segmentation and detection become an active point of research. Recently, deep neural networks (DNN) have seen a rapid advance in various medical image analysis fields, i.e., image registration, image segmentation, lesion detection, and shape modeling. Furthermore, convolution neural networks (CNN) have gained popularity in medical imaging, especially in brain imaging.
In this study, an automated technique is proposed to segment MS lesions in MRI. This technique depends on a 3D patchwise region-based convolution neural network (R-CNN) for MS lesion segmentation in T2-w and FLAIR.
The proposed method is evaluated using the public MICCAI2008 MS lesion segmentation data set, which is
compared to other MS lesion segmentation tools.  

DOI

10.21608/mjcis.2019.321063

Keywords

R-CNN, Deep learning, MS Segmentation

Authors

First Name

Doaa

Last Name

Al-Desouky

MiddleName

-

Affiliation

Faculty of Computers and Information, Computer Science Dept. Mansoura University, Egypt

Email

doaa.aldesouky@gmail.com

City

-

Orcid

-

First Name

Ehab

Last Name

Essa

MiddleName

-

Affiliation

Faculty of Computers and Information, Computer Science Dept. Mansoura University, Egypt

Email

ehab_essa@mans.edu.eg

City

-

Orcid

-

First Name

M. Z.

Last Name

Rashad

MiddleName

-

Affiliation

Faculty of Computers and Information, Computer Science Dept. Mansoura University, Egypt

Email

-

City

-

Orcid

-

First Name

Sherif E.

Last Name

Hussein

MiddleName

-

Affiliation

Faculty of Engineering, Computer and Systems Dept. Mansoura University, Egypt

Email

-

City

-

Orcid

-

Volume

15

Article Issue

2

Related Issue

43866

Issue Date

2019-12-01

Receive Date

2023-10-11

Publish Date

2019-12-01

Page Start

1

Page End

9

Print ISSN

2090-1666

Online ISSN

2090-1674

Article File

MJCIS_Volume 15_Issue 2_Pages 1-9.pdf

PDF . 908.3kB

Link

https://mjcis.journals.ekb.eg/article_321063.html

Detail API

https://mjcis.journals.ekb.eg/service?article_code=321063

Order

321,063

Type

Original Research Articles.

Type Code

1,784

Publication Type

Journal

Publication Title

Mansoura Journal for Computer and Information Sciences

Publication Link

https://mjcis.journals.ekb.eg/

MainTitle

Automatic Multiple Sclerosis lesion segmentation using Patch-wise R-CNN

Details

Type

Article

Created At

28 Dec 2024