Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury

Snehashis Roy; Sean Wilkes; Ramon Diaz-Arrastia M.D.; John A. Butman; Dzung L. Pham

doi:10.1117/12.2082199

20 March 2015 Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury

Snehashis Roy, Sean Wilkes, Ramon Diaz-Arrastia M.D., John A. Butman, Dzung L. Pham

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94130I (2015) https://doi.org/10.1117/12.2082199
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Quantification of hemorrhages in head computed tomography (CT) images from patients with traumatic brain injury (TBI) has potential applications in monitoring disease progression and better understanding of the patho-physiology of TBI. Although manual segmentations can provide accurate measures of hemorrhages, the processing time and inter-rater variability make it infeasible for large studies. In this paper, we propose a fully automatic novel pipeline for segmenting intraparenchymal hemorrhages (IPH) from clinical head CT images. Unlike previous methods of model based segmentation or active contour techniques, we rely on relevant and matching examples from already segmented images by trained raters. The CT images are first skull-stripped. Then example patches from an "atlas" CT and its manual segmentation are used to learn a two-class sparse dictionary for hemorrhage and normal tissue. Next, for a given "subject" CT, a subject patch is modeled as a sparse convex combination of a few atlas patches from the dictionary. The same convex combination is applied to the atlas segmentation patches to generate a membership for the hemorrhages at each voxel. Hemorrhages are segmented from 25 subjects with various degrees of TBI. Results are compared with segmentations obtained from an expert rater. A median Dice coefficient of 0.85 between automated and manual segmentations is achieved. A linear fit between automated and manual volumes show a slope of 1.0047, indicating a negligible bias in volume estimation.

Citation Download Citation

Snehashis Roy, Sean Wilkes, Ramon Diaz-Arrastia M.D., John A. Butman, and Dzung L. Pham "Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130I (20 March 2015); https://doi.org/10.1117/12.2082199

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