Combining Registration Errors and Supervoxel Classification for Unsupervised Brain Anomaly Detection

Automatic detection of brain anomalies in MR images is challenging and complex due to intensity similarity between lesions and healthy tissues as well as the large variability in shape, size, and location among different anomalies. Even though discriminative models (supervised learning) are commonly used for this task, they require quite high-quality annotated training images, which are absent for most medical image analysis problems. Inspired by groupwise shape analysis, we adapt a recent fully unsupervised supervoxel-based 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 outperforms a convolutional-autoencoder-based method and attains similar detection rates for hemispheric lesions in comparison to SAAD with substantially fewer false positives. It also presents promising detection scores for lesions in the cerebellum and brainstem.