A review on brain structures segmentation in magnetic resonance imaging

Sandra González-Villà, Arnau Oliver, Segi Valverde, Liping Wang, Reyer Zwiggelaar, Xavier Lladó

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

100 Dyfyniadau (Scopus)
876 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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 - 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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)45-69
CyfnodolynArtificial Intelligence in Medicine
Cyfrol73
Dyddiad ar-lein cynnar30 Medi 2016
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Hyd 2016

Ôl bys

Gweld gwybodaeth am bynciau ymchwil 'A review on brain structures segmentation in magnetic resonance imaging'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.

Dyfynnu hyn