Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images

Awais Mansoor; Rafael Casas Jr.; Marius G. Linguraru

doi:10.1117/12.2216958

24 March 2016 Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images

Awais Mansoor, Rafael Casas Jr., Marius G. Linguraru

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978514 (2016) https://doi.org/10.1117/12.2216958
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

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Awais Mansoor, Rafael Casas Jr., and Marius G. Linguraru "Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978514 (24 March 2016); https://doi.org/10.1117/12.2216958

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KEYWORDS

Image segmentation

Computed tomography

Chest

Lung

Tissues

Heart

Feature extraction

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