A review on automatic fetal and neonatal brain MRI segmentation
File(s)
Accepted version
Author(s)
Makropoulos, A
Counsell, SJ
Rueckert, D
Type
Journal Article
Abstract
In recent years, a variety of segmentation methods have been proposed for automatic delineation of the fetal and neonatal brain MRI. These methods aim to define regions of interest of different granularity: brain, tissue types or more localised structures. Different methodologies have been applied for this segmentation task and can be classified into unsupervised, parametric, classification, atlas fusion and deformable models. Brain atlases are commonly utilised as training data in the segmentation process. Challenges relating to the image acquisition, the rapid brain development as well as the limited availability of imaging data however hinder this segmentation task. In this paper, we review methods adopted for the perinatal brain and categorise them according to the target population, structures segmented and methodology. We outline different methods proposed in the literature and discuss their major contributions. Different approaches for the evaluation of the segmentation accuracy and benchmarks used for the segmentation quality are presented. We conclude this review with a discussion on shortcomings in the perinatal domain and possible future directions.
Date Issued
2017-06-28
Date Acceptance
2017-06-26
Citation
NeuroImage, 2017, 170, pp.231-248
ISSN
1053-8119
Publisher
Elsevier
Start Page
231
End Page
248
Journal / Book Title
NeuroImage
Volume
170
Copyright Statement
© 2017 Elsevier Inc. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Commission of the European Communities
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/28666878
PII: S1053-8119(17)30545-1
Grant Number
319456
Subjects
11 Medical And Health Sciences
17 Psychology And Cognitive Sciences
Neurology & Neurosurgery
Publication Status
Published
Coverage Spatial
United States