Cerebral microbleed segmentation from susceptibility weighted images

Snehashis Roy; Amod Jog; Elizabeth Magrath; John A. Butman; Dzung L. Pham

doi:10.1117/12.2082237

20 March 2015 Cerebral microbleed segmentation from susceptibility weighted images

Snehashis Roy, Amod Jog, Elizabeth Magrath, John A. Butman, Dzung L. Pham

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94131E (2015) https://doi.org/10.1117/12.2082237
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available