Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls

By Youngjin Yoo, Lisa Tang, Tom Brosch, David K. B. Li, Shannon H. Kolind, Irene M. Vavasour, Alexander Rauscher, Alex L. MacKay, Anthony Traboulsee, Roger C. Tam. 2017

88 citations recorded by the source corpus.

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t-test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Subfields: Epidemiology, Pathology and Forensic Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Multiple sclerosis [284] Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “assication. Such feature representation has been used in the classication of lung nodules into benign and malignant classes in CT [48], and in the identication of multiple sclerosis lesions in MRI [49]. Apart from the classication of radiological images, analysis of radiological text reports plays a”
Deep Learning for Medical Anomaly Detection – A Survey (2020, background): “Fusion of modalities has been a popular research direction which has recently emerged for MRI analysisIn [52] the authors investigate the fusion of T1-weighted (T1w) MRIs and myelin water imaging of MRIs (which is a quantitative MRI technique that specifically measures myelin content) for dignosis o”