Automatic Irregular Texture Detection in Brain MRI Without Human Supervision

We propose a novel approach named one-time sampling irregularity age map (OTS-IAM) to detect any irregular texture in FLAIR brain MRI without any human supervision or interaction. In this study, we show that OTS-IAM is able to detect FLAIR’s brain tissue irregularities (i.e. hyperintensities) without any manual labelling. One-time sampling (OTS) scheme is proposed in this study to speed up the computation. The proposed OTS-IAM implementation on GPU successfully speeds up IAM’s computation by more than 17 times. We compared the performance of OTS-IAM with two unsupervised methods for hyperintensities’ detection; the original IAM and the Lesion Growth Algorithm from public toolbox Lesion Segmentation Toolbox (LST-LGA), and two conventional supervised machine learning algorithms; support vector machine (SVM) and random forest (RF). Furthermore, we also compared OTS-IAM’s performance with three supervised deep neural networks algorithms; Deep Boltzmann machine (DBM), convolutional encoder network (CEN) and 2D convolutional neural network (2D Patch-CNN). Based on our experiments, OTS-IAM outperformed LST-LGA, SVM, RF and DBM while it was on par with CEN.