BADRESC: Brain Anomaly Detection based on Registration Errors and Supervoxel Classification

Abstract

Automatic detection of brain anomalies in MR images is very challenging and complex due to intensity similarity between lesions and normal tissues as well as the large variability in shape, size, and location among different anomalies. Inspired by groupwise shape analysis, we adapt a recent fully unsupervised supervoxelbased approach (SAAD) — designed for abnormal asymmetry detection of the hemispheres — to detect brain anomalies from registration errors. Our method, called BADRESC, extracts supervoxels inside the right and left hemispheres, cerebellum, and brainstem, models registration errors for each supervoxel, and treats outliers as anomalies. Experimental results on MR-T1 brain images of stroke patients show that BADRESC attains similar detection rate for hemispheric lesions in comparison to SAAD with substantially less false positives. It also presents promising detection scores for lesions in the cerebellum and brainstem.