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.

Fig. 1

Example CT scans of PE. The shown cases differ in severity (minimal, moderate, and severe from left to right). The pleural space is enclosed by visceral and parietal pleura. Note the similarity between the intensity of muscle and the intensity of PE.

Fig. 2

Schematic of the automatic PE segmentation process.

Fig. 3

Preprocessing and lung segmentation. (a) Original image. (b) After anisotropic diffusion. (c) Lung segmentation mask, green: right lung area, red: left lung area.

Fig. 4

Visceral and parietal pleura extraction. (a) Curve identified as visceral pleura. (b) Iterative refinement of the parietal pleura curve. (c) Slab model of the ribcage. (d) Curve identified as parietal pleura. (e) Resulting 2-D PE segmentation.

Fig. 5

Three-dimensional deformable surface model. (Top left) Two-dimensional segmentation result before the deformable surface model. (Top right) Two-dimensional segmentation result after the deformable surface model. (Bottom) Segmented PE surface [before (left) and after (right) deformable surface modeling].

Fig. 6

Comparison of segmentations. Manuals 1 and 2 were performed by the same fellow, months apart. Manual 3 was performed by a professional CT technologist. The automatic, manual left lung, and manual right lung segmentations are shown in yellow, brown, and pink, respectively.

Fig. 7

Correlations between automatic and manual segmentation volumes.

Fig. 8

Comparison of average errors. The average error (%) was calculated by dividing the difference between two segmentations by the mean total volume. The means and errors of these values from the 12-patient validation set are displayed.

Fig. 9

Bland–Altman plot comparing the automatic segmentation to manual segmentation 3.

Fig. 10

Distribution of PE volumes. The median was 90 cc, Q1 was 49 cc, and Q3 was 192 cc. For the left lung, the median was 103 cc, Q1 was 55 cc, and Q3 was 222 cc. For the right lung, the median was 85 cc, Q1 was 42 cc, and Q3 was 192 cc.

Fig. 11

Comparison of measured volume with radiologist estimates: 0 means that the radiologist described the effusion as resolved or nonexistent; 1 means they described it as “tiny,” “very small,” or “minimal”; 2, 3, and 4 mean they described it as “small,” “moderate,” and “large,” respectively.