HighlightsLesion segmentation is an important tool in multiple sclerosis (MS) research.Several automated methods exist, e.g., supervised, trained or deep learning-based.Convolutional neural networks (CNN) show promising results for lesion segmentation.CNN method nicMSlesions can be trained with input from one single-subject.With minimal input, nicMSlesions outperforms e.g. LST-LPA and LesionTOADS.

Introduction Atrophy of the spinal cord is known to occur in multiple sclerosis (MS). The mean upper cervical cord area (MUCCA) can be used to measure this atrophy. Currently, several (semi-)automated methods for MUCCA measurement exist, but validation in clinical magnetic resonance (MR) images is lacking. Methods Five methods to measure MUCCA (SCT-PropSeg, SCT-DeepSeg, NeuroQLab, Xinapse JIM and ITK-SNAP) were investigated in a predefined upper cervical cord region. First, within-scanner reproducibility and between-scanner robustness were assessed using intra-class correlation coefficient (ICC) and Dice's similarity index (SI) in scan-rescan 3DT1-weighted images (brain, including cervical spine using a head coil) performed on three 3 T MR machines (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) in 21 subjects with MS and 6 healthy controls (dataset A). Second, sensitivity of MUCCA measurement to lesions in the upper cervical cord was assessed with cervical 3D T1-weighted images (3 T GE HDxT using a head-neck-spine coil) in 7 subjects with MS without and 14 subjects with MS with cervical lesions (dataset B), using ICC and SI with manual reference segmentations. Results In dataset A, MUCCA differed between MR machines ( p < 0.001) and methods ( p < 0.001) used, but not between scan sessions. With respect to MUCCA values, Xinapse JIM showed the highest within-scanner reproducibility (ICC absolute agreement = 0.995) while Xinapse JIM and SCT-PropSeg showed the highest between-scanner robustness (ICC consistency = 0.981 and 0.976, respectively). Reproducibility of segmentations between scan sessions was highest in Xinapse JIM and SCT-PropSeg segmentations (median SI ≥ 0.921), with a significant main effect of method ( p < 0.001), but not of MR machine or subject group. In dataset B, SI with manual outlines did not differ between patients with or without cervical lesions for any of the segmentation methods ( p > 0.176). However, there was an effect of method for both volumetric and voxel wise agreement of the segmentations (both p < 0.001). Highest volumetric and voxel wise agreement was obtained with Xinapse JIM (ICC absolute agreement = 0.940 and median SI = 0.962). Conclusion Although MUCCA is highly reproducible within a scanner for each individual measurement method, MUCCA differs between scanners and between methods. Cervical cord lesions do not affect MUCCA measurement performance.

Background:There is currently no consensus about the extent of gray matter (GM) atrophy that can be attributed to secondary changes following white matter (WM) lesions, or the temporal and spatial relationships between the two phenomena. Elucidating this interplay will broaden the understanding of the combined inflammatory and neurodegenerative pathophysiology of multiple sclerosis (MS), and separating atrophic changes due to primary and secondary neurodegenerative mechanisms will then be pivotal to properly evaluate treatment effects, especially if these treatments target the different processes individually.Objective:To untangle these complex pathological mechanisms, this systematic review provides an essential first step: an objective and comprehensive overview of the existing in vivo knowledge of the relationship between brain WM lesions and GM atrophy, in patients diagnosed with MS. The overall aim was to clarify the extent to which WM lesions associate with both global and regional GM atrophy, and how this may differ in the different disease subtypes.Methods:We searched MEDLINE (through PubMed) and Embase for reports containing direct associations between brain GM and WM lesion measures obtained by conventional MRI sequences in patients with clinically isolated syndrome (CIS) and MS. No restriction was applied for publication date. The quality and risk of bias in included studies was evaluated using the Quality Assessment Tool for observational cohort and cross-sectional studies (NIH, Bethesda, MA). Qualitative and descriptive analyses were performed.Results:A total of 90 articles were included. WM lesion volumes were mostly related to global, cortical and deep GM volumes, and those significant associations were almost without exception negative, indicating that higher WM lesion volumes were associated with lower GM volumes or lower cortical thicknesses. The most consistent relation between WM lesions and GM atrophy was seen in early (relapsing) disease, and less so in progressive MS.Conclusion:The findings suggest that GM neurodegeneration is mostly secondary to damage in the WM during early disease stages, while becoming more detached and dominated by other, possibly primary neurodegenerative disease mechanisms, in progressive MS.

Background: In early multiple sclerosis (MS), thalamus atrophy and decreased integrity of the thalamocortical white matter (WM) tracts have been observed. Objective: To investigate the temporal association between thalamus volume and WM damage in the thalamocortical tract in subjects with early MS. Methods: At two time points, 72 subjects with early MS underwent T1, FLAIR and diffusion tensor imaging. Thalamocortical tracts were identified with probabilistic tractography using left and right thalamus as seed regions. Regression analysis was performed to identify predictors of annual percentage change in both thalamus volumes and integrity of the connected tracts. Results: Significant atrophy was seen in left and right thalamus ( p < 0.001) over the follow-up period (13.7 ± 4.8 months), whereas fractional anisotropy (FA) and mean diffusivity (MD) changes of the left and right thalamus tracts were not significant, although large inter-subject variability was seen. Annual percentage change in left thalamus volume was significantly predicted by baseline FA of the left thalamus tracts F (1.71) = 4.284, p = 0.042; while no such relation was found for the right thalamus. Annual percentage change in FA or MD of the thalamus tracts was not predicted by thalamus volume or any of the demographic parameters. Conclusion: Over a short follow-up time, thalamus atrophy could be predicted by decreased integrity of the thalamic tracts, but changes in the integrity of the thalamic tracts could not be predicted by thalamus volume. This is the first study showing directionality in the association between thalamus atrophy and connected WM tract damage. These results need to be verified over longer follow-up periods.

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