Automatic segmentation and measurement of pleural effusions on CT.

Abstract

Pleural effusion is an important biomarker for the diagnosis of many diseases. We develop an automated method to evaluate pleural effusion on CT scans, the measurement of which is prohibitively time consuming when performed manually. The method is based on parietal and visceral pleura extraction, active contour models, region growing, Bezier surface fitting, and deformable surface modeling. Twelve CT scans with three manual segmentations were used to validate the automatic segmentation method. The method was then applied on 91 additional scans for visual assessment. The segmentation method yielded a correlation coefficient of 0.97 and a Dice coefficient of 0.72±0.13 when compared to a professional manual segmentation. The visual assessment estimated 83% cases with negligible or small segmentation errors, 14% with medium errors, and 3% with large errors.