Share Email Print

Proceedings Paper

Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images
Author(s): Awais Mansoor; Rafael Casas Jr.; Marius G. Linguraru
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Pleural effusion is an abnormal collection of fluid within the pleural cavity. Excessive accumulation of pleural fluid is an important bio-marker for various illnesses, including congestive heart failure, pneumonia, metastatic cancer, and pulmonary embolism. Quantification of pleural effusion can be indicative of the progression of disease as well as the effectiveness of any treatment being administered. Quantification, however, is challenging due to unpredictable amounts and density of fluid, complex topology of the pleural cavity, and the similarity in texture and intensity of pleural fluid to the surrounding tissues in computed tomography (CT) scans. Herein, we present an automated method for the segmentation of pleural effusion in CT scans based on spatial context information. The method consists of two stages: first, a probabilistic pleural effusion map is created using multi-atlas segmentation. The probabilistic map assigns a priori probabilities to the presence of pleural uid at every location in the CT scan. Second, a statistical pattern classification approach is designed to annotate pleural regions using local descriptors based on a priori probabilities, geometrical, and spatial features. Thirty seven CT scans from a diverse patient population containing confirmed cases of minimal to severe amounts of pleural effusion were used to validate the proposed segmentation method. An average Dice coefficient of 0.82685 and Hausdorff distance of 16.2155 mm was obtained.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978514 (24 March 2016); doi: 10.1117/12.2216958
Show Author Affiliations
Awais Mansoor, Children's National Medical Ctr. (United States)
Rafael Casas Jr., National Institute of Health (United States)
Marius G. Linguraru, Children's National Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?