MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features

• Published in: IEEE transactions on medical imaging Vol. 38; no. 11; pp. 2545 - 2555
• Main Authors: Minaee, Shervin, Wang, Yao, Aygar, Alp, Chung, Sohae, Wang, Xiuyuan, Lui, Yvonne W., Fieremans, Els, Flanagan, Steven, Rath, Joseph
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Brain; Coders; Concrete; Deep learning; diffusion MRI; Feature extraction; Head injuries; Learning algorithms; Machine learning; Magnetic resonance imaging; Measurement; Medical imaging; MTBI identification; Neural networks; Public health; Traumatic brain injury; Visualization; White matter
• DOI: 10.1109/TMI.2019.2905917
• ISSN: 0278-0062; 1558-254X

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.