TY - GEN
T1 - Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network
AU - Ang, Sui Paul
AU - Phung, Son Lam
AU - Schira, Mark Matthias
AU - Bouzerdoum, Abdesselam
AU - Duong, Soan Thi Minh
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.
AB - Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.
KW - brain tissue segmentation
KW - convolutional neural network
KW - deep learning
KW - functional MRI
KW - long short-term memory
UR - http://www.scopus.com/inward/record.url?scp=85062231545&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2018.8615850
DO - 10.1109/DICTA.2018.8615850
M3 - Conference contribution
AN - SCOPUS:85062231545
T3 - 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
BT - 2018 International Conference on Digital Image Computing
A2 - Pickering, Mark
A2 - Zheng, Lihong
A2 - You, Shaodi
A2 - Rahman, Ashfaqur
A2 - Murshed, Manzur
A2 - Asikuzzaman, Md
A2 - Natu, Ambarish
A2 - Robles-Kelly, Antonio
A2 - Paul, Manoranjan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Y2 - 10 December 2018 through 13 December 2018
ER -