Headache classification and automatic biomarker extraction from structural MRIs using deep learning

作者：Md Mahfuzur Rahman Siddiquee, Jay Shah, Catherine Daniela Chong, Simona Nikolova, Gina M. Dumkrieger, Baojie Li, Teresa Q. Wu, Todd J. Schwedt · 发表于：Brain Communications · 年份：2022 · DOI：10.1093/braincomms/fcac311 · 被引用次数：34 · 研究领域：Migraine and Headache Studies、Trigeminal Neuralgia and Treatments、Traumatic Brain Injury and Neurovascular Disturbances

Abstract Data-driven machine-learning methods on neuroimaging (e.g. MRI) are of great interest for the investigation and classification of neurological diseases. However, traditional machine learning requires domain knowledge to delineate the brain regions first, followed by feature extraction from the regions. Compared with this semi-automated approach, recently developed deep learning methods have advantages since they do not require such prior knowledge; instead, deep learning methods can automatically find features that differentiate MRIs from different cohorts. In the present study, we developed a deep learning-based classification pipeline distinguishing brain MRIs of individuals with one of three types of headaches [migraine (n = 95), acute post-traumatic headache (n = 48) and persistent post-traumatic headache (n = 49)] from those of healthy controls (n = 532) and identified the brain regions that most contributed to each classification task. Our pipeline included: (i) data preprocessing; (ii) binary classification of healthy controls versus headache type using a 3D ResNet-18; and (iii) biomarker extraction from the trained 3D ResNet-18. During the classification at the second step of our pipeline, we resolved two common issues in deep learning methods, limited training data and imbalanced samples from different categories, by incorporating a large public data set and resampling among the headache cohorts. Our method achieved the following classification accuracies wh...