An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation

Abstract

Multiple sclerosis (MS) is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of multiple sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones segmentation performed using a new Gaussian mixture model based on various databases atlases. Afterward, lesion segmentation begins with the estimation of a lesion map, which is then subjected to threshold constraints and refined by a new lesion expansion algorithm. The evaluation was carried out on four clinical databases integrating various clinical cases which had different lesion loads and were presented by a set of MRI modalities at several noise levels. The results compared with those of the existing methods proved excellent cerebral segmentation with dice averages close to 0.8 and sensitivity and specificity averages greater than 0.9. In addition, depending on the used database, the lesion segmentation recorded mean values were close to or greater than 0.8 for the different metrics. The detection error and outline error averages were about 0.3. Besides the ability to identify the lesions affecting the different parts of the brain, even those spreading in the gray matter, the proposed methodology identified the lesions cores and their surrounding vasogenic edema. This has been thoroughly tested and validated by highly qualified radiologists and neurologists. The evaluation of the resulting discriminations recorded values close to or greater than 0.9 for dice, sensitivity, and specificity. As a valuable benefit, a computer aided diagnosis tool could be offered to clinicians. It would help efficiently during the MS diagnosis and avoid several confusions. Besides, it could be used for longitudinal survey and henceforth extends to other pathologies that could be explored by MRI modalities, such as glioblastoma or alzheimer's disease.

Radius-optimized efficient template matching for lesion detection from brain images

“…Some research groups designed two-stage segmentation tasks; they first determine the putative lesion voxels with the help of various techniques such as unsupervised mixture models 42 , estimation of lesion and tissue label using Markov random field (MRF) 43 , cascaded convolutional neural network (CNN) 44 , etc., and then segment the lesions by classifying the voxels. In another two-step approach, brain tissues, mainly WM, GM, and CSF, were segmented using different techniques such as atlas-based EM 37 , Gaussian mixture model (GMM) 45 , SPM8/12 segmentation algorithm 46 , etc., before delineating the lesion while making use of the segmented tissues. These multistage approaches generally require multi-contrast MRI.…”

“…The values presented in Tables 2 and 3 were retrieved from the literature and leaderboard results ( http://www.ia.unc.edu/MSseg/re-sults_table.php ), and show that high FPR is very common among the methods even when the computation is done with multi-contrast MRI. Ghribi et al 45 pointed out the poor quality of MR images in MSLS 2008 as the reason behind the high FPR of their method. As expected, supervised learning methods performed well in both tables compared to unsupervised techniques.…”

Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images

“…Some research groups designed two-stage segmentation tasks; they first determine the putative lesion voxels with the help of various techniques such as unsupervised mixture models 42 , estimation of lesion and tissue label using Markov random field (MRF) 43 , cascaded convolutional neural network (CNN) 44 , etc., and then segment the lesions by classifying the voxels. In another two-step approach, brain tissues, mainly WM, GM, and CSF, were segmented using different techniques such as atlas-based EM 37 , Gaussian mixture model (GMM) 45 , SPM8/12 segmentation algorithm 46 , etc., before delineating the lesion while making use of the segmented tissues. These multistage approaches generally require multi-contrast MRI.…”

Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study

“…2 A number of automatic segmentation techniques have been proposed for tissue segmentation in MS. 3 However, majority of these methods focused only on lesion segmentation and/or were based on a single-center study. These methods have shown only modest accuracy when applied to multi-center data (see, for example, previous studies [4][5][6][7][8] and references therein). Besides lesions, volumes of GM, WM, and cerebrospinal fluid (CSF) are also shown to be affected by the disease state in MS (see the recent review).…”