Automated Segmentation Method of White Matter and Gray Matter Regions with Multiple Sclerosis Lesions in MR Images

Overview

Journal Radiol Phys Technol

Specialties Biophysics
Biotechnology
Radiology

Date 2010 Oct 1

PMID 20882375

Citations 2

Authors

Taiki Magome

Hidetaka Arimura

Shingo Kakeda

Daisuke Yamamoto

Yasuo Kawata

Yasuo Yamashita

Yoshiharu Higashida

Fukai Toyofuku

Masafumi Ohki

Yukunori Korogi

Affiliations

Soon will be listed here.

Abstract

Our purpose in this study was to develop an automated method for segmentation of white matter (WM) and gray matter (GM) regions with multiple sclerosis (MS) lesions in magnetic resonance (MR) images. The brain parenchymal (BP) region was derived from a histogram analysis for a T1-weighted image. The WM regions were segmented by addition of MS candidate regions, which were detected by our computer-aided detection system for the MS lesions, and subtraction of a basal ganglia and thalamus template from "tentative" WM regions. The GM regions were obtained by subtraction of the WM regions from the BP region. We applied our proposed method to T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery images acquired from 7 MS patients and 7 control subjects on a 3.0 T MRI system. The average similarity indices between the specific regions obtained by our method and by neuroradiologists for the BP and WM regions were 95.5 ± 1.2 and 85.2 ± 4.3%, respectively, for MS patients. Moreover, they were 95.0 ± 2.0 and 85.9 ± 3.4%, respectively, for the control subjects. The proposed method might be feasible for segmentation of WM and GM regions in MS patients.

Citing Articles

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.

Mecheter I, Alic L, Abbod M, Amira A, Ji J J Digit Imaging. 2020; 33(5):1224-1241.

PMID: 32607906 PMC: 7573060. DOI: 10.1007/s10278-020-00361-x.

Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR.

Zhong Y, Utriainen D, Wang Y, Kang Y, Haacke E Int J Biomed Imaging. 2014; 2014:239123.

PMID: 25136355 PMC: 4130152. DOI: 10.1155/2014/239123.

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