Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892, USA.
Med Phys. 2011 Dec;38(12):6633-42. doi: 10.1118/1.3662918.
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.
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.
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.
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%.
评估一种计算机辅助检测（CAD）系统在非泻剂 CT 结肠成像（CTC）中检测结肠息肉的性能，该系统结合了基于图像的自动结肠清洁算法。
设计了一种自动结肠清洁算法，用于检测和减去标记粪便，以解决异质性和标记不良的问题，与结肠 CAD 系统一起使用。该方法具有局部适应性，将强度、形状和纹理分析与概率优化相结合。无泻剂肠道准备的 CTC 数据用于测试和训练参数。患者在扫描前 48 小时内分剂量服用钡剂或 Gastroview。未给予泻药，也无需饮食调整。从富含息肉的队列中选择病例，包括至少 90%的固体粪便被视觉估计为标记的扫描，并且每个结肠段在俯卧或仰卧位均充分扩张。CAD 系统在有和没有粪便减法算法的情况下进行比较。
数据集包含 19 名患者的 38 个俯卧和/或仰卧位 CTC 扫描，包含 44 个大于 10mm 的息肉（如果在俯卧位和仰卧位扫描中匹配，则为 22 个独特的息肉）。该算法在褶皱周围的精细细节、结肠壁上的薄粪便衬里、靠近息肉和大的液体/粪便池方面具有稳健的性能。在不使用粪便减法模块的情况下，CAD 系统的每息肉敏感性为 70.5%，假阳性率为 5.75 个/扫描。在非泻剂数据上自动结肠清洁后，检测性能显著提高（p=0.009），真阳性率为 86.4%，假阳性率为 5.75 个/扫描。
一种设计用于克服非泻剂结肠挑战的自动基于图像的结肠清洁算法，可将结肠 CAD 的敏感性提高约 15%。
