The influence of MOGAD on diagnosis of multiple sclerosis using MRI.

Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is an immune-mediated demyelinating disease that is challenging to differentiate from multiple sclerosis (MS), as the clinical phenotypes overlap, and people with MOGAD can fulfil the current MRI-based diagnostic criteria for MS. In addition, the MOG antibody assays that are an essential component of MOGAD diagnosis are not standardized. Accurate diagnosis of MOGAD is crucial because the treatments and long-term prognosis differ from those for MS.

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study.

Background And Rationale: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining.

Methods: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls.

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.

Atypical idiopathic inflammatory demyelinating lesions: prognostic implications and relation to multiple sclerosis.

Atypical lesions of a presumably idiopathic inflammatory demyelinating origin present quite variably and may pose diagnostic problems. The subsequent clinical course is also uncertain. We, therefore, wanted to clarify if atypical idiopathic inflammatory demyelinating lesions (AIIDLs) can be classified according to previously suggested radiologic characteristics and how this classification relates to prognosis.

Incorporating domain knowledge into the fuzzy connectedness framework: application to brain lesion volume estimation in multiple sclerosis.

A method for incorporating prior knowledge into the fuzzy connectedness image segmentation framework is presented. This prior knowledge is in the form of probabilistic feature distribution and feature size maps, in a standard anatomical space, and "intensity hints" selected by the user that allow for a skewed distribution of the feature intensity characteristics. The fuzzy affinity between pixels is modified to encapsulate this domain knowledge.