Translator Disclaimer
20 March 2015 Cerebral microbleed segmentation from susceptibility weighted images
Author Affiliations +
Abstract
Cerebral microbleeds (CMB) are a common marker of traumatic brain injury. Accurate detection and quantification of the CMBs are important for better understanding the progression and prognosis of the injury. Previous microbleed detection methods have suffered from a high rate of false positives, which is time consuming to manually correct. In this paper, we propose a fully automatic, example-based method to segment CMBs from susceptibility-weighted (SWI) scans, where examples from an already segmented template SWI image are used to detect CMBs in a new image. First, multiple radial symmetry transforms (RST) are performed on the template SWI to detect small ellipsoidal structures, which serve as potential microbleed candidates. Then 3D patches from the SWI and its RSTs are combined to form a feature vector at each voxel of the image. A random forest regression is trained using the feature vectors, where the dependent variable is the binary segmentation voxel of the template. Once the regression is learnt, it is applied to a new SWI scan, whose feature vectors contain patches from SWI and its RSTs. Experiments on 26 subjects with mild to severe brain injury show a CMB detection sensitivity of 85:7%, specificity 99:5%, and a false positive to true positive ratio of 1:73, which is competitive with published methods while providing a significant reduction in computation time.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Snehashis Roy, Amod Jog, Elizabeth Magrath, John A. Butman, and Dzung L. Pham "Cerebral microbleed segmentation from susceptibility weighted images", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131E (20 March 2015); https://doi.org/10.1117/12.2082237
PROCEEDINGS
7 PAGES


SHARE
Advertisement
Advertisement
Back to Top