Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set

• Published in: Magnetic resonance imaging Vol. 62; pp. 18 - 27
• Main Authors: Bento, Mariana, Souza, Roberto, Salluzzi, Marina, Rittner, Letícia, Zhang, Yunyan, Frayne, Richard
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.10.2019
• Subjects: Adult; Aged; Aged, 80 and over; Atherosclerosis - diagnostic imaging; Atherosclerosis - pathology; Brain - diagnostic imaging; Brain - pathology; Brain image processing; Carotid Arteries - diagnostic imaging; Carotid artery atherosclerotic disease; Cohort Studies; Feature extraction; Female; Humans; Machine learning; Magnetic Resonance Imaging; Male; Middle Aged; Multi-center data set; Multiple Sclerosis - diagnostic imaging; Neuroimaging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Support Vector Machine; White Matter - diagnostic imaging; Multi-center data set; Feature extraction; Brain image processing; Machine learning; Carotid artery atherosclerotic disease
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2019.06.007

Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging.