scholarly journals 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

2020 ◽  
Vol 267 (12) ◽  
pp. 3541-3554 ◽  
Author(s):  
Alexandra de Sitter ◽  
◽  
Tom Verhoeven ◽  
Jessica Burggraaff ◽  
Yaou Liu ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Majority Voting ◽  
Dice Similarity Coefficient ◽  
Lesion Volume ◽  
Impaired Performance ◽  
Segmentation Methods ◽  
Deep Grey Matter ◽  
Global And Local ◽  
Automated Methods

Abstract 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.

scholarly journals Contrasting the brain imaging features of MOG-antibody disease, with AQP4-antibody NMOSD and multiple sclerosis

2021 ◽  
pp. 135245852110189
Author(s):  
Silvia Messina ◽  
Romina Mariano ◽  
Adriana Roca-Fernandez ◽  
Ana Cavey ◽  
Maciej Jurynczyk ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Diffusion Imaging ◽  
White Matter Lesions ◽  
Lesion Volume ◽  
Imaging Features ◽  
Strongly Correlated ◽  
Deep Grey Matter ◽  
Magnetic Resonance Imaging Mri ◽  
Aqp4 Antibody

Background: Identifying magnetic resonance imaging (MRI) markers in myelin-oligodendrocytes-glycoprotein antibody-associated disease (MOGAD), neuromyelitis optica spectrum disorder-aquaporin-4 positive (NMOSD-AQP4) and multiple sclerosis (MS) is essential for establishing objective outcome measures. Objectives: To quantify imaging patterns of central nervous system (CNS) damage in MOGAD during the remission stage, and to compare it with NMOSD-AQP4 and MS. Methods: 20 MOGAD, 19 NMOSD-AQP4, 18 MS in remission with brain or spinal cord involvement and 18 healthy controls (HC) were recruited. Volumetrics, lesions and cortical lesions, diffusion-imaging measures, were analysed. Results: Deep grey matter volumes were lower in MOGAD ( p = 0.02) and MS ( p = 0.0001), compared to HC and were strongly correlated with current lesion volume (MOGAD R = −0.93, p < 0.001, MS R = −0.65, p = 0.0034). Cortical/juxtacortical lesions were seen in a minority of MOGAD, in a majority of MS and in none of NMOSD-AQP4. Non-lesional tissue fractional anisotropy (FA) was only reduced in MS ( p = 0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways. Conclusion: MOGAD patients are left with grey matter damage, and this may be related to persistent white matter lesions. NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, but reduced non-lesional tissue FA. Observations from our study can be used to identify new markers of damage for future multicentre studies.

Deep grey matter T2 hypo-intensity in patients with paediatric multiple sclerosis

2011 ◽  
Vol 17 (6) ◽  
pp. 702-707 ◽  
Author(s):  
Antonia Ceccarelli ◽  
Maria A Rocca ◽  
Elisabetta Perego ◽  
Lucia Moiola ◽  
Angelo Ghezzi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Caudate Nucleus ◽  
Grey Matter ◽  
Lesion Volume ◽  
Longitudinal Assessment ◽  
Paediatric Patients ◽  
Relapsing Remitting ◽  
Left Caudate ◽  
Deep Grey Matter ◽  
Dual Echo

Objective: T2 hypo-intensity on magnetic resonance imaging scans is thought to reflect pathological iron deposition in the presence of disease. In this pilot study, we evaluated the utility of the quantification of T2 hypo-intensities in paediatric patients by estimating deep grey matter (DGM) T2 hypo-intensities in paediatric patients with multiple sclerosis (MS) or clinically isolated syndromes (CIS), and their changes over 1 year. Methods: A dual-echo sequence was obtained from 45 paediatric patients (10 with CIS, 35 with relapsing–remitting MS, 8 with an onset of the disease before the age of 10 and 37 during adolescence) and 14 age-matched healthy controls (HC). Eleven patients were reassessed both clinically and with MRI after 1 year. Normalized T2 intensity in the basal ganglia and thalamus was quantified. Results: At baseline, DGM T2 intensity was similar between paediatric patients and HC in all the structures analysed, except for the head of the left caudate nucleus ( p = 0.001). DGM T2 intensity of the head of the left caudate nucleus was similar between paediatric CIS and RRMS patients, but it was reduced in adolescent-onset paediatric patients versus HC ( p = 0.002). In all patients, DGM T2 intensity of the head of the left caudate nucleus was correlated with T2 lesion volume ( r = −0.39, p = 0.007). DGM T2 intensity in all the structures analysed with longitudinal assessment remained stable over the follow-up in the cohort of patients. Conclusions: The quantification of DGM T2 intensity in paediatric patients may provide surrogate markers of neurodegeneration. In paediatric MS, DGM is likely to be affected by iron-related changes, which are likely to be, at least partially, secondary to WM damage.

scholarly journals Contrasting the brain imaging features of MOG-Antibody disease, with AQP4-Antibody NMOSD and Multiple Sclerosis

2020 ◽  
Author(s):  
Silvia Messina ◽  
Romina Mariano ◽  
Adriana Roca-Fernandez ◽  
Ana Cavey ◽  
Maciej Jurynczyk ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Visual Acuity ◽  
White Matter ◽  
Grey Matter ◽  
Optic Chiasm ◽  
Lesion Volume ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Cortical Lesions ◽  
Deep Grey Matter

Neuromyelitis optica associated with aquaporin-4-antibodies (NMOSD-AQP4) and myelin oligodentrocyte-glycoprotein antibody-associated disorder (MOGAD) have been recently recognised as different from multiple sclerosis. Although conventional MRI may help distinguish multiple sclerosis from antibody-mediated diseases, the use of quantitative and non-conventional imaging may give more pathological information and explain the clinical differences. We compared, using non-conventional imaging, brain MRI findings in 75 subjects in remission with NMOSD-AQP4, MOGAD, multiple sclerosis or healthy controls (HC). Volumetrics, white matter and cortical lesions, and tissue integrity measures using diffusion imaging, were analysed in the four groups along with their association with disability (expanded disability status scale [EDSS] and visual acuity). The volumetric analysis showed that, deep grey matter volumes were significantly lower in multiple sclerosis (p=0.0001) and MOGAD (p=0.02), compared to HC. Relapsing MOGAD had lower white matter, pallidus and hippocampus volumes than in monophasic (p<0.05). Optic chiasm volume was reduced only in NMOSD-AQP4 who had at least one episode of optic neuritis (ON) (NMOSD-AQP4-ON vs NMOSD-AQP4 p<0.001, HC p<0.001, MOGAD-ON p=0.04, multiple sclerosis-ON p=0.02) likely reflecting the recognised posterior location of NMOSD-AQP4-ON and its severity. Lesion volume was greatest in multiple sclerosis followed by MOGAD and in these two diseases, the lesion volume correlated with disease duration (multiple sclerosis R=0.46, p=0.05, MOGAD R=0.81, p<0.001), cortical thickness (multiple sclerosis R=-0.64, p=0.0042, MOGAD=-0.71, p=0.005) and deep grey matter volumes (multiple sclerosis R=-0.65, p=0.0034, MOGAD R=-0.93, p<0.001). Lesional-fractional anisotropy (FA) was reduced and mean diffusivity increased in all patients, but overall, FA was only reduced in the non-lesional tissue in multiple sclerosis (p=0.01), although focal reductions were noted in NMOSD-AQP4, reflecting mainly optic nerve and corticospinal tract pathways. Cortical/juxtacortical lesions were seen in a minority of MOGAD, while cortical/juxtacortical and purely cortical lesions were identified in the majority of multiple sclerosis and in none of the NMOSD-AQP4. Non-lesional FA in NMOSD-AQP4, lower white-matter volume and female sex in multiple sclerosis, and lower brainstem volume in MOGAD were the best predictors of EDSS disability (accounting for 46%, 49% and 19% respectively). Worse visual acuity associated with lower optic chiasm volume in NMOSD-AQP4 and lower thalamus volume in MOGAD (accounting for 58% and 35% respectively). Although MOGAD patients had good outcomes, deep grey matter atrophy was present. In contrast, NMOSD-AQP4 patients showed a relative sparing of deep grey matter volumes, despite greater residual disability as compared with MOGAD patients. NMOSD-AQP4 but not MOGAD patients showed reduced FA in non-lesional tissue.

Memory impairment in multiple sclerosis: correlation with deep grey matter and mesial temporal atrophy

2009 ◽  
Vol 80 (2) ◽  
pp. 201-206 ◽  
Author(s):  
R H B Benedict ◽  
D Ramasamy ◽  
F Munschauer ◽  
B Weinstock-Guttman ◽  
R Zivadinov
Keyword(s):  
Multiple Sclerosis ◽  
Memory Impairment ◽  
Grey Matter ◽  
Deep Grey Matter

scholarly journals Brain atrophy and employment in multiple sclerosis patients: a 10-year follow-up study

2020 ◽  
Vol 6 (1) ◽  
pp. 205521732090248
Author(s):  
Cecilie Jacobsen ◽  
Robert Zivadinov ◽  
Kjell-Morten Myhr ◽  
Turi O Dalaker ◽  
Ingvild Dalen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Grey Matter ◽  
Resonance Imaging ◽  
Cortical Volume ◽  
Deep Grey Matter ◽  
Multiple Sclerosis Patients ◽  
The Unemployed

Background Multiple sclerosis is often associated with unemployment. The contribution of grey matter atrophy to unemployment is unclear. Objectives To identify magnetic resonance imaging biomarkers of grey matter and clinical symptoms associated with unemployment in multiple sclerosis patients. Methods Demographic, clinical data and 1.5 T magnetic resonance imaging scans were collected in 81 patients at the time of inclusion and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. Statistical analysis was performed using a mixed linear model. Results At baseline 31 (38%) of the patients were unemployed, at 5-year follow-up 44 (59%) and at 10-year follow-up 34 (81%) were unemployed. The unemployed patients had significantly lower subcortical deep grey matter volume ( P < 0.001), specifically thalamus, pallidus, putamen and hippocampal volumes, and cortical volume ( P = 0.011); and significantly greater T1 ( P < 0.001)/T2 ( P < 0.001) lesion volume than the employed patient group at baseline. Subcortical deep grey matter volumes, and to a lesser degree cortical volume, were significantly associated with unemployment throughout the follow-up. Conclusion We found significantly greater atrophy of subcortical deep grey matter and cortical volume at baseline and during follow-up in the unemployed patient group. Atrophy of subcortical deep grey matter showed a stronger association to unemployment than atrophy of cortical volume during the follow-up.

scholarly journals Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

2020 ◽  
pp. 135245852092136 ◽  
Author(s):  
Ivan Coronado ◽  
Refaat E Gabr ◽  
Ponnada A Narayana
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Network Performance ◽  
Three Dimensional ◽  
Proton Density ◽  
Dice Similarity Coefficient ◽  
Lesion Volume ◽  
Imaging Data ◽  
Post Contrast ◽  
Fluid Attenuated Inversion Recovery

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

scholarly journals Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

2020 ◽  
Vol 14 ◽  
Author(s):  
Chenyi Zeng ◽  
Lin Gu ◽  
Zhenzhong Liu ◽  
Shen Zhao
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Review Of The Literature ◽  
Lesion Segmentation ◽  
Segmentation Methods ◽  
Future Direction

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

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