Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

BACKGROUND: Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied. METHODS: On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed. RESULTS: ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations. CONCLUSIONS: Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

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.