Volumetric detection of flat lesions for minimal-preparation dual-energy CT colonography

Computer-aided detection (CAD) systems for computed tomographic colonography (CTC) tend to miss many flat lesions. We developed a volumetric method for automated detection of lesions with dual-energy CTC (DECTC). The target region for the detection is defined in terms of a distance transform of the colonic lumen. To detect lesions, volumetric shape features are calculated at the image scale defined by the thickness of the target region. False-positive (FP) detections are reduced by use of a random-forest classifier based on shape, texture, and dual-energy features of the detected lesion candidates. For pilot evaluation, 37 patients were examined by use of DE-CTC with a reduced one-day bowel preparation. The CAD scheme was trained with the DE-CTC data of 12 patients, and it was tested with the DE-CTC data of 25 patients. The detection sensitivity was assessed at multiple thicknesses of the target region. There were 39 lesions ≥6 mm in 15 patients, including 8 flat lesions ≥10 mm. The thickness of the target region had a statistically significant effect on the detection sensitivity. At the optimal thickness of the target region, the per-lesion and per-patient sensitivities for flat lesions were 100% at a median of 4 FPs per patient.