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Magn Reson Imaging ; 2019 Apr 17.
Artigo em Inglês | MEDLINE | ID: mdl-31004711


This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or less depending on the homogeneity of the dataset). Two use-case scenarios for segmenting the spinal cord white and grey matter are presented: one in marmosets with variable numbers of lesions, and the other in the publicly available human grey matter segmentation challenge [1]. The pipeline is freely available at:

Mult Scler ; : 1352458518822408, 2019 Feb 07.
Artigo em Inglês | MEDLINE | ID: mdl-30730246


BACKGROUND:: Experimental autoimmune encephalomyelitis (EAE) in the common marmoset is a nonhuman primate model of multiple sclerosis (MS) that shares numerous clinical, radiological, and pathological features with MS. Among the clinical features are motor and sensory deficits that are highly suggestive of spinal cord (SC) damage. OBJECTIVE:: To characterize the extent and nature of SC damage in symptomatic marmosets with EAE using a combined magnetic resonance imaging (MRI) and histopathology approach. MATERIALS AND METHODS:: SC tissues from five animals were scanned using 7 T MRI to collect high-resolution ex vivo images. Lesions were segmented and classified based on shape, size, and distribution along the SC. Tissues were processed for histopathological characterization (myelin and microglia/macrophages). Statistical analysis, using linear mixed-effects models, evaluated the association between MRI and histopathology. RESULTS:: Marmosets with EAE displayed two types of SC lesions: focal and subpial lesions. Both lesion types were heterogeneous in size and configuration and corresponded to areas of marked demyelination with high density of inflammatory cells. Inside the lesions, the MRI signal was significantly correlated with myelin content ( p < 0.001). CONCLUSIONS:: Our findings underscore the relevance of this nonhuman primate EAE model for better understanding mechanisms of MS lesion formation in the SC.

Brain ; 142(3): 633-646, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-30715195


Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Neuroimage ; 184: 901-915, 2019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-30300751


The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Processamento de Imagem Assistida por Computador/métodos , Esclerose Múltipla/diagnóstico por imagem , Esclerose Múltipla/patologia , Redes Neurais (Computação) , Medula Espinal/patologia , Humanos , Imagem por Ressonância Magnética/métodos , Variações Dependentes do Observador , Reconhecimento Automatizado de Padrão , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
Brain ; 141(6): 1637-1649, 2018 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-29688408


Multiple sclerosis is an inflammatory demyelinating disease of the central nervous system. Although it has been extensively studied, the proximate trigger of the immune response remains uncertain. Experimental autoimmune encephalomyelitis in the common marmoset recapitulates many radiological and pathological features of focal multiple sclerosis lesions in the cerebral white matter, unlike traditional experimental autoimmune encephalomyelitis in rodents. This provides an opportunity to investigate how lesions form as well as the relative timing of factors involved in lesion pathogenesis, especially during early stages of the disease. We used MRI to track experimental autoimmune encephalomyelitis lesions in vivo to determine their age, stage of development, and location, and we assessed the corresponding histopathology post-mortem. We focused on the plasma protein fibrinogen-a marker for blood-brain barrier leakage that has also been linked to a pathogenic role in inflammatory demyelinating lesion development. We show that fibrinogen has a specific spatiotemporal deposition pattern, apparently deriving from the central vein in early experimental autoimmune encephalomyelitis lesions <6 weeks old, and preceding both demyelination and visible gadolinium enhancement on MRI. Thus, fibrinogen leakage is one of the earliest detectable events in lesion pathogenesis. In slightly older lesions, fibrinogen is found inside microglia/macrophages, suggesting rapid phagocytosis. Quantification demonstrates positive correlation of fibrinogen deposition with accumulation of inflammatory cells, including microglia/macrophages and T cells. The peak of fibrinogen deposition coincides with the onset of demyelination and axonal loss. In samples from chronic multiple sclerosis cases, fibrinogen was found at the edge of chronic active lesions, which have ongoing demyelination and inflammation, but not in inactive lesions, suggesting that fibrinogen may play a role in sustained inflammation even in the chronic setting. In summary, our data support the notion that fibrinogen is a key player in the early pathogenesis, as well as sustained inflammation, of inflammatory demyelinating lesions.