A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

TL;DR

This paper presents an unsupervised deep learning method using diffusion MRI and a bag of words approach to detect mild traumatic brain injury, outperforming traditional mean-based metrics.

Contribution

It introduces a novel unsupervised deep learning framework with patch-level feature learning and bag of words representation for MTBI detection from diffusion MRI.

Findings

Patch-level features outperform mean value metrics.

Unsupervised auto-encoder effectively learns relevant features.

Method achieves comparable or better performance than previous approaches.

Abstract

Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.