Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

Objective: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients.

Methods: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing-remitting MS patients, as a part of a phase 3 randomized clinical trial.

The Neurocognitive and MRI Outcomes of West Nile Virus Infection: Preliminary Analysis Using an External Control Group.

To understand the long-term neurological outcomes resultant of West Nile virus (WNV) infection, participants from a previously established, prospective WNV cohort were invited to take part in a comprehensive neurologic and neurocognitive examination. Those with an abnormal exam finding were invited for MRI to evaluate cortical thinning and regional brain atrophy following infection. Correlations of presenting clinical syndrome with neurologic and neurocognitive dysfunctions were evaluated, as well as correlations of neurocognitive outcomes with MRI results.

Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study.

A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting.

Is 3D MPRAGE better than the combination DIR/PSIR for cortical lesion detection at 3T MRI?

Background And Objectives: Based on the application of newer magnetic resonance imaging (MRI) acquisition sequences, the detection of cortical lesions (CL) in multiple sclerosis (MS) has significantly improved. Double inversion recovery (DIR) at 3T has increased the detection sensitivity and classification specificity when combined with phase sensitive inversion recovery (PSIR). Previous findings with 3D magnetization prepared rapid acquisition with gradient echo (MPRAGE) sequences, showed improved classification specificity of purely intracortical (IC) and mixed (MX) lesions, compared to the classification based on DIR/PSIR.

Regional gray matter atrophy in relapsing remitting multiple sclerosis: baseline analysis of multi-center data.

Regional gray matter (GM) atrophy in multiple sclerosis (MS) at disease onset and its temporal variation can provide objective information regarding disease evolution. An automated pipeline for estimating atrophy of various GM structures was developed using tensor based morphometry (TBM) and implemented on a multi-center sub-cohort of 1008 relapsing remitting MS (RRMS) patients enrolled in a Phase 3 clinical trial. Four hundred age and gender matched healthy controls were used for comparison.

A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis.

Accurate classification and quantification of brain tissues is important for monitoring disease progression, measurement of atrophy, and correlating magnetic resonance (MR) measures with clinical disability. Classification of MR brain images in the presence of lesions, such as multiple sclerosis (MS), is particularly challenging. Images obtained with lower resolution often suffer from partial volume averaging leading to false classifications.

Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis.

Background: Longitudinal magnetic resonance imaging (MRI) studies show that a fraction of the multiple sclerosis (MS) T2-lesions contain T1-hypointense components that may persist to represent severe, irreversible tissue damage. It is not known why certain lesions convert to persistent T1-hypointense lesions.

Objective: We hypothesized that the T1-hypointense lesions disproportionately distribute in the more hypoperfused areas of the brain.

Magnetic resonance imaging outcomes from a phase III trial of teriflunomide.

Objective: The purpose of this study was to determine the effects of oral teriflunomide on multiple sclerosis (MS) pathology inferred by magnetic resonance imaging (MRI).

Methods: Patients (n=1088) with relapsing MS were randomized to once-daily teriflunomide 7 mg or 14 mg, or placebo, for 108 weeks. MRI was recorded at baseline, 24, 48, 72 and 108 weeks.

Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study.

A comprehensive analysis of the global and regional values of cortical thickness based on 3D magnetic resonance images was performed on 250 relapsing remitting multiple sclerosis (MS) patients who participated in a multi-center, randomized, phase III clinical trial (the CombiRx Trial) and 125 normal controls. The MS cohort was characterized by relatively low clinical disability and short disease duration. An automatic pipeline was developed for identifying images with poor quality and artifacts.

Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS.

Multiple sclerosis (MS) is the most common immune-mediated disabling neurological disease of the central nervous system. The pathogenesis of MS is not fully understood. Histopathology implicates both demyelination and axonal degeneration as the major contributors to the accumulation of disability.

Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis.

Background: Accurate classification of multiple sclerosis (MS) lesions in the brain cortex may be important in understanding their impact on cognitive impairment (CI). Improved accuracy in identification/classification of cortical lesions was demonstrated in a study combining two magnetic resonance imaging (MRI) sequences: double inversion recovery (DIR) and T1-weighted phase-sensitive inversion recovery (PSIR).

Objective: To evaluate the role of intracortical lesions (IC) in MS-related CI and compare it with the role of mixed (MX), juxtacortical (JX), the sum of IC + MX and with total lesions as detected on DIR/PSIR images.

Automated brain extraction from T2-weighted magnetic resonance images.

Purpose: To develop and implement an automated and robust technique to extract brain from T2-weighted images.

Materials And Methods: Magnetic resonance imaging (MRI) was performed on 75 adult volunteers to acquire dual fast spin echo (FSE) images with fat-saturation technique on a 3T Philips scanner. Histogram-derived thresholds were derived directly from the original images followed by the application of regional labeling, regional connectivity, and mathematical morphological operations to extract brain from axial late-echo FSE (T2-weighted) images.

Comprehensive brain MRI segmentation in high risk preterm newborns.

Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods.

Effect of cocaine on structural changes in brain: MRI volumetry using tensor-based morphometry.

Magnetic resonance imaging (MRI) was performed in cocaine-dependent subjects to determine the structural changes in brain compared to non-drug using controls. Cocaine-dependent subjects and controls were carefully screened to rule out brain pathology of undetermined origin. Magnetic resonance images were analyzed using tensor-based morphometry (TBM) and voxel-based morphometry (VBM) without and with modulation to adjust for volume changes during normalization.

Improved cerebellar tissue classification on magnetic resonance images of brain.

Purpose: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.

Materials And Methods: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template.

Symmetric inverse consistent nonlinear registration driven by mutual information.

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE).

Deep gray matter atrophy in multiple sclerosis: a tensor based morphometry.

Tensor based morphometry (TBM) was applied to determine the atrophy of deep gray matter (DGM) structures in 88 relapsing multiple sclerosis (MS) patients. For group analysis of atrophy, an unbiased atlas was constructed from 20 normal brains. The MS brain images were co-registered with the unbiased atlas using a symmetric inverse consistent nonlinear registration.

Mutual information driven inverse consistent nonlinear registration.

A fully symmetric nonlinear viscoelastic image registration method, under the demons paradigm is developed. The symmetric cost function includes mutual information (MI) as a similarity measure, regularization of the transformation, and inverse consistent constraint (ICC). Alternative strategy is used to minimize the divergent terms with different properties in the cost function to avoid the difficulties in balancing between the performance of registration and low Inverse Consistency Error (ICE).

Information measures-based intensity standardization of MRI.

Scan-to-scan intensity variation, even with the same imaging modality, affects a number of intensity-based image processing methods such as feature map based segmentation and non-rigid registration techniques that minimize sum of squared differences (SSD). Current intensity standardization techniques based on either percentile alignment or polynomial mapping suffer from a number of limitations. We present a novel intensity standardization techniques that exploits information measures obtained from the images.

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations.

Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis.

Purpose: To develop and implement a method for identification and quantification of gadolinium (Gd) enhancements with minimal human intervention.

Materials And Methods: Dual fast spin echo (FSE), fluid attenuation inversion recovery (FLAIR), and pre- and postcontrast T1-weighted spin echo were acquired on 22 subjects. The enhancements were identified on the postcontrast T1-weighted images based on morphological operations.

Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI.

An adaptive fuzzy c-means (FCM) clustering algorithm is explored for segmentation of three-dimensional (3D) multi-spectral MR images. This algorithm takes into consideration of both noise and 3D intensity non-uniformity. This algorithm models the intensity nonuniformity of MR images as a gain field or bias field that slowly varies in space, which is approximated by a linear combination of smooth basis functions made up of polynomials with different orders.

Unified approach for multiple sclerosis lesion segmentation on brain MRI.

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images.

Segmentation and quantification of black holes in multiple sclerosis.

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images.