Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis.

Objective.The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of the Primary Progressive versus the Relapsing-Remitting state of disease based on diffusion and structural magnetic resonance imaging data.Approach.A selection of microstructural descriptors, based on the 3D-Simple Harmonics Oscillator Based Reconstruction and Estimation and the set of new algebraically independent Rotation Invariant spherical harmonics Features, was considered and used to feed convolutional neural networks (CNNs) models. Classical measures derived from diffusion tensor imaging, that are fractional anisotropy and mean diffusivity, were used as benchmark for diffusion MRI (dMRI). Finally, T1-weighted images were also considered for the sake of comparison with the state-of-the-art. A CNN model was fit to each feature map and layerwise relevance propagation (LRP) heatmaps were generated for each model, target class and subject in the test set. Average heatmaps were calculated across correctly classified patients and size-corrected metrics were derived on a set of regions of interest to assess the LRP contrast between the two classes.Main results.Our results demonstrated that dMRI features extracted in grey matter tissues can help in disambiguating primary progressive multiple sclerosis from relapsing-remitting multiple sclerosis patients and, moreover, that LRP heatmaps highlight areas of high relevance which relate well with what is known from literature for MS disease.Significance.Within a patient stratification task, LRP allows detecting the input voxels that mostly contribute to the classification of the patients in either of the two classes for each feature, potentially bringing to light hidden data properties which might reveal peculiar disease-state factors.

Computational methods for corpus callosum segmentation on MRI: A systematic literature review.

BACKGROUND AND OBJECTIVE: The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging. METHODS: IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process. RESULTS: This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T1-weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images. CONCLUSIONS: The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.

Magnetic resonance imaging in neuropsychiatric systemic lupus erythematosus: current state of the art and novel approaches.

Systemic lupus erythematosus is a chronic, inflammatory, immune-mediated disease affecting 0.1% of the general population. Neuropsychiatric manifestations in systemic lupus erythematosus have been more frequently recognized and reported in recent years, occurring in up to 75% of patients during the disease course. Magnetic resonance imaging is known to be a useful tool for the detection of structural brain abnormalities in neuropsychiatric systemic lupus erythematosus patients because of the excellent soft-tissue contrast observed with MRI and the ability to acquire multiplanar images. In addition to conventional magnetic resonance imaging techniques to evaluate the presence of atrophy and white matter lesions, several different magnetic resonance imaging techniques have been used to identify microstructural or functional abnormalities. This review will highlight different magnetic resonance imaging techniques, including the advanced magnetic resonance imaging methods used to determine central nervous system involvement in systemic lupus erythematosus.