Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution

Our purpose in this study was to develop an automated segmentation scheme for multiple sclerosis (MS) lesions in magnetic resonance images using an artificial neural network (ANN)-controlled level-set method. Forty-nine slices with T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images were selected from six examinations of three MS patients including 168 MS lesions for this study. First, MS lesions were enhanced by background subtraction. Initial regions of MS candidates were detected based on a multiple-gray-level thresholding technique and a region-growing technique on the subtraction image. Then, final regions of MS candidates were determined by application of a proposed segmentation method using an ANN-controlled level-set method, which was used for reduction of false positives (FPs) as well as more accurate segmentation. Finally, all candidate regions were classified into true positive and FP candidate regions by use of a support vector machine. As the result of a leave-one-candidate-out test method, the detection sensitivity for MS lesions increased from 64.9 to 75.0% while decreasing the number of FPs per slice from 19.9 to 4.4 compared with a previous study. The proposed scheme improved the sensitivity and the number of FPs in the detection of MS lesions.

The Segmentation is a fundamental technique used in image processing to extract suspicious regions from a given image. Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. Recently, numerous methods have been introduced for the segmentation of MS lesions in MR images. In this work, we propose to segment the images based on a constrained Gaussian Mixture Model (GMM) and using Genetic Algorithm to determine the optimal parameters of this highly non-linear model. We use this method for automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) of magnetic resonance images. Moreover, a preprocessing step is also performed to suppress artifacts and to remove the unwanted skull portions from brain MRI. The proposed method has been tested on real patients' MR images. The experimental results show that the proposed method can effectively segment MS lesions in the brain MR images.

Background: In natalizumab-treated multiple sclerosis (MS) patients, magnetic resonance imaging (MRI) is considered as a sensitive tool in detecting both MS disease activity and progressive multifocal leukoencephalopathy (PML). Objective: To investigate the performance of neuroradiologists using brain MRI in detecting new MS lesions and asymptomatic PML lesions and in differentiating between MS and PML lesions in natalizumab-treated MS patients. The secondary aim was to investigate interrater variability. Methods: In this retrospective diagnostic study, four blinded neuroradiologists assessed reference and follow-up brain MRI scans of 48 natalizumab-treated MS patients with new asymptomatic PML lesions (n = 21) or new MS lesions (n = 20) or no new lesions (n = 7). Sensitivity and specificity for detection of new lesions in general (MS and PML lesions), MS and PML lesion differentiation, and PML detection were determined. Interrater agreement was calculated. Results: Overall sensitivity and specificity for the detection of new lesions, regardless of the nature of the lesions, were 77.4% and 89.3%, respectively; for PML-MS lesion differentiation, 74.2% and 84.7%, respectively; and for asymptomatic PML lesion detection, 59.5% and 91.7%, respectively. Interrater agreement for the tested categories was fair to moderate. Conclusion: The diagnostic performance of trained neuroradiologists using brain MRI in pharmacovigilance of natalizumab-treated MS patients is moderately good. Interrater agreement among trained readers is fair to moderate.

Oligodendrocyte Progenitor Cell Susceptibility to Injury in Multiple Sclerosis

Multiple sclerosis: Classification revisited reveals homogeneity and recapitulation

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered ”normal”. Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions’ segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

The precise location of multiple sclerosis (MS) cortical lesions can be very challenging at 3 T, yet distinguishing them from subcortical lesions is essential for the diagnosis and prognosis of the disease. Compressed sensing-accelerated fluid and white matter suppression imaging (CS-FLAWS) is a new magnetic resonance imaging sequence derived from magnetization-prepared 2 rapid acquisition gradient echo with promising features for the detection and classification of MS lesions. The objective of this study was to compare the diagnostic performances of CS-FLAWS (evaluated imaging) and phase sensitive inversion recovery (PSIR; reference imaging) for classification of cortical lesions (primary objective) and infratentorial lesions (secondary objective) in MS, in combination with 3-dimensional (3D) double inversion recovery (DIR). Prospective 3 T scans (MS first diagnosis or follow-up) acquired between March and August 2021 were retrospectively analyzed. All underwent 3D CS-FLAWS, axial 2D PSIR, and 3D DIR. Double-blinded reading sessions exclusively in axial plane and final consensual reading were performed to assess the number of cortical and infratentorial lesions. Wilcoxon test was used to compare the 2 imaging datasets (FLAWS + DIR and PSIR + DIR), and intraobserver and interobserver agreement was assessed using the intraclass correlation coefficient. Forty-two patients were analyzed (38 with relapsing-remitting MS, 29 women, 42.7 ± 12.6 years old). Compressed sensing-accelerated FLAWS allowed the identification of 263 cortical lesions versus 251 with PSIR ( P = 0.74) and 123 infratentorial lesions versus 109 with PSIR ( P = 0.63), corresponding to a nonsignificant difference between the 2 sequences. Compressed sensing-accelerated FLAWS exhibited fewer false-negative findings than PSIR either for cortical lesions (1 vs 13; P < 0.01) or infratentorial lesions (1 vs 15; P < 0.01). No false-positive findings were found with any of the 2 sequences. Diagnostic confidence was high for each contrast. Three-dimensional CS-FLAWS is as accurate as 2D PSIR imaging for classification of cortical and infratentorial MS lesions, with fewer false-negative findings, opening the way to a reliable full brain MS exploration in a clinically acceptable duration (5 minutes 15 seconds).

icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

Purpose: To prospectively determine and compare the sensitivity in the detection of multiple sclerosis (MS) lesions using Dual Inversion-Recovery (DIR), fluid-attenuated inversion recovery (FLAIR), and a T2-weighted turbo spin echo (T2 TSE) magnetic resonance imaging (MRI) at 3.0T in patients with suspected and definite MS.

Objectives: Mechanisms of angiogenesis regulate multiple sclerosis (MS) lesions’ evolution, displaying both neuroprotective and harmful effects. Factors traditionally considered as purely angiogenic, like vascular endothelial growth factor (VEGF), exert complex heterogenous actions on both neural and vascular malformation-derived tissues. Aim of this retrospective study was to examine, for the first time, potential associations between the presence of common vascular malformations, like vertebral hemangiomas (VHs), and several clinico-radiological MS parameters.Methods: 236 MS patients who were followed in our Outpatient Clinic were recruited in this study. Outcome measures concerned demographics, disease-derived variables, and MS-lesions’ distribution in VHs – positive and negative patients. All data were collected retrospectively. Potential correlations were assessed with univariate statistical analyses (p = 0.05), followed by multivariate regression models, for purposes of confounder-effects elimination.Results: VH presence showed significant negative correlations with presence of MS lesions in the thoracic (p = 0.005 for thoracic VHs), but not the cervical cord. Trends towards negative associations of VH presence with subtentorial MS lesions and positive family history for MS were also observed.Discussion: Our observations suggest that VH presence may reduce the risk of thoracic demyelinating lesions in MS patients. They could be explained as part of a multifaceted angiogenic process, concomitantly enhancing neural repair and abnormal hemangioma vascularization.

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

MTR and T1 relaxation times are abnormal in MS lesions and NAWM, and may reflect tissue damage such as demyelination and axonal loss. Their relationship and potential to provide complementary information in tissue characterisation is explored. The aim of this study was to document the relationship between magnetisation transfer ratio (MTR) and T1 relaxation time in Multiple Sclerosis (MS) lesions and normal appearing white matter (NAWM) in order to determine whether the combination provides a more comprehensive tissue characterisation than either parameter in isolation. Ten patients with relapsing remitting MS and 10 age matched healthy controls underwent imaging using a protocol which included the measurement of both MTR and T1 relaxation times. The MTR and T1 values were compared statistically using a commonly adopted correlation approach and a mixed-model regression approach. There was a strong correlation between MTR and T1 in MS lesions (r=0.74). The correlation was seen equally in T1 hypointense and isointense lesions. The relationship was much weaker in MS NAWM (r=0.24) and no correlation was found in control white matter (r=0.06). Mixed-model regression analysis confirmed that the relationship between T1 and MTR is strongly dependent upon tissue type (MS lesion, MS NAWM, or control white matter). The relationship between MTR and T1 relaxation time measurements varies markedly between pathological and normal tissue types. In MS, the complementary information obtained from MTR and T1 is most apparent in NAWM. The results emphasise the potential for combinations of MR parameters to improve tissue characterisation, which in turn should improve understanding of disease pathology and treatment monitoring. Multiple Sclerosis (2000) 6 327 - 331

Magnetic Resonance Imaging (MRI) is an important paraclinical tool for diagnosing and following-up of Multiple Sclerosis (MS). The detection of MS lesions in MRI may require complementary information to filter false detections. Given that MS lesions cannot be located within cerebrospinal fluid (CSF), detection of this region is very helpful for our purpose. Although T1-weighted images are usually chosen to detect CSF regions, the gray level similarity between some MS lesions and CSF regions difficult this task. With the aim of discriminating CSF region within intracranial region, but considering aforementioned drawback, we propose a fuzzy-based algorithm that involves the regional analysis of the fuzzy information obtained from a previous local analysis. The proposed algorithm introduces location, shape and size constraints in CSF detection, and provides confidence degrees associated with the possibility of including MS lesion pixels.KeywordsMagnetic resonance imagingbrainmultiple sclerosiscerebrospinal fluidfuzzy setsregional analysis

Multiple sclerosis (MS) lesions are histopathologically characterized by inflammation, demyelination/remyelination, axonal damage and gliosis. Animal experimental and in vitro studies suggest that sex hormones influence the immune system and contribute to the increased likelihood in women of developing MS. However, a variety of studies have also shown that remyelination is more marked in female rodents or that female sex hormones are beneficial for myelin repair. To determine whether gender influences the histopathology of MS lesions, we compared the extent of inflammation, axonal damage and remyelination in MS lesions of female and male MS patients. We observed no differences in the composition of inflammatory infiltrates, axonal damage or cortical pathology. Similar numbers of oligodendroglial progenitor cells and mature oligodendrocytes were present in MS lesions. Remyelination is slightly, but not significantly, more extensive in women than men in early MS lesions. The absence of significant differences in lesion pathology between female and male MS patients might be explained by a lack of a gender influence, but also might be due to the limited number of tissue samples available for histopathological analysis.

Brain tissue is an intricate anatomical structure and hence thorough detection of numerous brain ailments much relies on precise segmentation of three major tissues, viz., cerebro-spinal fluid (CSF), gray matter (GM) and white matter (WM) in MR brain images. This problem has been addressed in literature, but many key open issues still remains to be investigated. As an initial stride in this development, an automated method for segmentation of deformities like atrophy and tumor in brain MR images is developed. The paper next concentrates on segmenting multiple sclerosis (MS) lesions in WM of central nervous system (CNS). A modified algorithm that relies on the histon based fast fuzzy C-means (HFFCM) is developed. In the former, the experimentation is carried out using brain web datasets and in the latter, the datasets used were from the MICCAI grand challenge II workshop for segmenting MS lesions. The results obtained from the proposed algorithms were compared with the existing methods using performance metrics such as specificity, sensitivity, accuracy, relative absolute volume difference (RAVD), average symmetric absolute surface distance (ASASD) etc. It is observed that the results of segmentation accuracies from the proposed methods were very high when compared with the existing methods.