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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proc. BIOIMAGING |
| Publisher | INSTICC Press |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2020 |
Keywords
- Brain Anomaly Detection
- Supervoxel Segmentation
- One-class Classification
- Registration Errors
- MRI