Authors:
Samuel B. Martins
1
;
2
;
3
;
Alexandre X. Falcão
1
and
Alexandru C. Telea
4
Affiliations:
1
Laboratory of Image Data Science (LIDS), Institute of Computing, University of Campinas, Brazil
;
2
Bernoulli Institute, University of Groningen, The Netherlands
;
3
Federal Institute of São Paulo, Campinas, Brazil
;
4
Department of Information and Computing Sciences, Utrecht University, The Netherlands
Keyword(s):
Brain Anomaly Detection, Supervoxel Segmentation, One-class Classification, Registration Errors, MRI.
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 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 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.