Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Makropoulos:2017:10.1016/j.neuroimage.2017.06.074,
author = {Makropoulos, A and Counsell, SJ and Rueckert, D},
doi = {10.1016/j.neuroimage.2017.06.074},
journal = {NeuroImage},
pages = {231--248},
title = {A review on automatic fetal and neonatal brain MRI segmentation},
url = {http://dx.doi.org/10.1016/j.neuroimage.2017.06.074},
volume = {170},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Makropoulos,A
AU  - Counsell,SJ
AU  - Rueckert,D
DO  - 10.1016/j.neuroimage.2017.06.074
EP  - 248
PY  - 2017///
SN  - 1053-8119
SP  - 231
TI  - A review on automatic fetal and neonatal brain MRI segmentation
T2  - NeuroImage
UR  - http://dx.doi.org/10.1016/j.neuroimage.2017.06.074
UR  - https://www.ncbi.nlm.nih.gov/pubmed/28666878
UR  - http://hdl.handle.net/10044/1/50004
VL  - 170
ER  -
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