Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

Abstract

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.

Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.

Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.

Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Figure 1

A typical example of cathartic-free tagged CT of the colon with (a) homogeneously well-tagged stool, (b) thin linings of high-attenuation contrast agent along the colonic walls and folds, and (c) a pool of heterogeneous stool with irregular tagging and dark air bubbles.

Figure 2

Methodology flow chart for the automated image-based colon cleansing. After the segmentation of the colon area, an initial labeling of the image into air, tissue, and stool is performed in the single material classification based on image intensity and texture features. Then, the quadratic regression models the transitions between materials and shape analysis corrects for the pseudo-enhancement of colonic folds. The probabilities of materials are updated during expectation maximization before subtracting the stool from the image. The steps of the algorithm are presented in detail below.

Figure 3

A typical distribution of material histograms from one CTC case with tagged stool. The distributions are normalized by their maximum value. The high peaks of the unclassified distribution likely represent the transitions between tissue-stool and tissue-air, which are affected by pseudo-enhancement and partial volume effect. The transition between air-stool has a more ambiguous distribution partly overlapping with the tissue histogram.

Figure 4

Quadratic regression. The three parabolas in the picture are based on (Ref. 33) with additional modifications introduced from the adaptive local computation of S_max.

Figure 5

Results are illustrated at the intermediate steps of the colon cleansing method. The original CT (A) is processed after colon mask segmentation in (B), adaptive thresholding in (C), texture computation in (D), single material classification in (E), and identification of material transitions in (F). In (C), the blocks represent regions of locally computed intensity thresholds; brighter colors represent higher values in (C) and (D). (D) Illustrates the identification of areas of high texture. (E) Depicts the classification of air, tissue and stool separated by unclassified materials (white). (F) Illustrates the classification of air, tissue, stool, and tissue-air, stool-air and tissue-stool transitions.

Figure 6

An example of submerged folds in heterogeneous stool; (a) is the original CTC image, (b) the results of the Hessian shape analysis with enhancement of submerged folds, and (c) is the final after colon cleansing. Note the variability in tagging in (a) with air bubbles enclosed by stool and the correct preservation of the colonic folds in (c).

Figure 7

An example of automated image-based colon cleansing. The original CTC image in (a) corresponds to Fig. 5; the result of our stool removal algorithm is illustrated in (b).

Figure 8

Two examples of CTC data with polyps (circles) before (a and c) and after (b and d) automated colon cleansing.

Figure 9

A typical example of flythrough virtual colonoscopy before (A) and after (B) the automated colon-cleansing. Pictures on the bottom row show the location of the virtual colonoscope in the CTC image: (C) and (D) correspond to (A) and (B) respectively in the coronal view. Images were generated using the V3D-Colon visualization package [Viatronix, Stony Brook, NY] and approximated to match by the location and camera view. Note that (A) shows the flythrough after the automated removal of tagged material by the visualization software (V3D-Colon) with residuals, while (C) shows the uncleansed data. (B) and (D) present the automated cleansing results using the proposed method. The arrow indicates the position of the virtual camera.

Figure 10

An example of flythrough virtual colonoscopy with an image of a colonic lesion. Pictures on the bottom row show the location of the virtual colonoscope in the CTC image: (C), (E) and (F) correspond to (A) and (D), (G) and (H) correspond to (B) in axial, coronal and sagittal views, respectively. Images were generated using the V3D-Colon visualization package [Viatronix, Stony Brook, NY] and approximated to match by the location and camera view. Note that (A) shows the flythrough after the automated removal of tagged material by the visualization software (V3D-Colon) with residuals, while (C), (E) and (F) show the uncleansed data. (B), (D), (G) and (H) present the automated cleansing results using the proposed method. The arrow indicates the position of the virtual camera. (E), (F), (G) and (H) are close-up views around the colonic lesion.

Figure 11

The comparative FROC curves using colonic polyp CAD on cathartic-free data with and without automated colon cleansing. The results are significantly improved when the proposed method for colon cleansing is employed.

Figure 12

An example of erroneous colon cleansing. (a) is a CTC image presenting poorly tagged heterogeneous stool and (b) shows the erroneous cleansing. The untagged stool resembles submerged folds both by shape and appearance and is directly attached to the colonic wall, which makes its removal very challenging.