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A review on brain structures segmentation in magnetic resonance imaging

Background and objectives Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches. Methods We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods. We further discuss each category’s strengths and weaknesses and analyze its performance in segmenting different brain structures providing a qualitative and quantitative comparison. Results We compare the results of the analyzed works for the following brain structures: hippocampus, thalamus, caudate nucleus, putamen, pallidum, amygdala, accumbens, lateral ventricles, and brainstem. The structures on which more works have focused on are the hippocampus and the caudate nucleus. In general, the accumbens (0.69 mean DSC) is the most difficult structure to segment whereas the structures that seem to get the best results are the brainstem, closely followed by the thalamus and the putamen with 0.88, 0.87 and 0.86 mean DSC, respectively. Atlas-based approaches achieve good results when segmenting the hippocampus (DSC between 0.75 and 0.90), thalamus (0.88–0.92) and lateral ventricles (0.83–0.93), while deformable methods perform good for caudate nucleus (0.84–0.91) and putamen segmentation (0.86–0.89). Conclusions There is not yet a single automatic segmentation approach that can emerge as a standard for the clinical practice, providing accurate brain structures segmentation. Future trends need to focus on combining multi-atlas methods with learning-based or deformable approaches. Employing atlases to provide spatial robustness and modeling the structures appearance with supervised classifiers or Active Appearance Models could lead to improved segmentation results

AcknowledgementsS. González-Villà holds a UdG-BRGR2015 grant from the Uni-versity of Girona. This work has been partially supported by “LaFundació la Marató de TV3”, by Retos de Investigación TIN2014-55710-R, and by MPC UdG 2016/022 grant

© Artificial Intelligence in Medicine, 2016, vol. 73, p. 45-69

Elsevier

Manager: Ministerio de Economía y Competitividad (Espanya)
Author: González Villà, Sandra
Oliver i Malagelada, Arnau
Valverde Valverde, Sergi
Wang, Liping
Zwiggelaar, Reyer
Lladó Bardera, Xavier
Date: 2016 October 1
Abstract: Background and objectives Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches. Methods We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods. We further discuss each category’s strengths and weaknesses and analyze its performance in segmenting different brain structures providing a qualitative and quantitative comparison. Results We compare the results of the analyzed works for the following brain structures: hippocampus, thalamus, caudate nucleus, putamen, pallidum, amygdala, accumbens, lateral ventricles, and brainstem. The structures on which more works have focused on are the hippocampus and the caudate nucleus. In general, the accumbens (0.69 mean DSC) is the most difficult structure to segment whereas the structures that seem to get the best results are the brainstem, closely followed by the thalamus and the putamen with 0.88, 0.87 and 0.86 mean DSC, respectively. Atlas-based approaches achieve good results when segmenting the hippocampus (DSC between 0.75 and 0.90), thalamus (0.88–0.92) and lateral ventricles (0.83–0.93), while deformable methods perform good for caudate nucleus (0.84–0.91) and putamen segmentation (0.86–0.89). Conclusions There is not yet a single automatic segmentation approach that can emerge as a standard for the clinical practice, providing accurate brain structures segmentation. Future trends need to focus on combining multi-atlas methods with learning-based or deformable approaches. Employing atlases to provide spatial robustness and modeling the structures appearance with supervised classifiers or Active Appearance Models could lead to improved segmentation results
AcknowledgementsS. González-Villà holds a UdG-BRGR2015 grant from the Uni-versity of Girona. This work has been partially supported by “LaFundació la Marató de TV3”, by Retos de Investigación TIN2014-55710-R, and by MPC UdG 2016/022 grant
Format: application/pdf
Citation: 025909
ISSN: 0933-3657 (versió paper)
1873-2860 (versió electrònica)
Document access: http://hdl.handle.net/10256/13179
Language: eng
Publisher: Elsevier
Collection: MINECO/PE 2015-2017/TIN2014-55710-R
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.artmed.2016.09.001
Articles publicats (D-ATC)
Is part of: © Artificial Intelligence in Medicine, 2016, vol. 73, p. 45-69
Rights: Tots els drets reservats
Subject: Imatgeria per ressonància magnètica
Magnetic resonance imaging
Malalties cerebrovasculars -- Imatges per ressonància magnètica
Cerebrovascular disease -- Magnetic resonance imaging
Imatges -- Segmentació
Imaging segmentation
Imatges -- Processament -- Tècniques digitals
Image processing -- Digital techniques
Imatgeria mèdica
Imaging systems in medicine
Title: A review on brain structures segmentation in magnetic resonance imaging
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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