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 taggedstool, 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 ¼ 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%.

Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study

“…Early EC methods consisted of mathematical models based on an explicit segmentation of the fecal materials on CTC images [ 6 , 7 , 8 ]. Such methods produce many image artifacts [ 5 ] because the virtual cleansing is performed by subtracting the segmented regions from the images.…”

Electronic Cleansing in Fecal-Tagging Dual-Energy CT Colonography Based on Material Decomposition and Virtual Colon Tagging

“…After more than a decade of investigation, EC has reached a level of sophistication that allows for visualization of the entire colonic surface [11]. Various EC methods have been developed, including voxel classification [12], [13], gradient-based edge detection [7], [14], material transition [15], structure analysis [16], mosaic decomposition [17], and hybrid methods [18]- [20]. However, existing EC methods tend to generate artifacts and pitfalls in cleansed CTC images, which impair the diagnostic performance of cleansed CTC images [21], [22].…”

Volumetric detection of colorectal lesions for noncathartic dual-energy computed tomographic colonography

“…Conventional visualization and image processing tools provide limited assistance in noncathartic CTC, because such tools have been developed for cathartic fluid-tagging CTC that does not have the challenging technical problems of noncathartic CTC [16]. Only few attempts have been made to develop dedicated computer-assisted reading tools for noncathartic CTC [6], [18], [3], [10]. …”