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Radiologists can outperform computer-aided detection (CAD) systems for CT colonography, because they consider not only local characteristics but also the context of findings. In particular, isolated findings are considered as more suspicious than clustered ones. We developed a computational method to model this problem-solving technique for reducing false-positive (FP) CAD detections in CT colonography. Lesion likelihood was estimated from shape and texture features of each candidate detection by use of a Bayesian neural network. Context features were calculated to characterize the distribution of candidate detections in a local neighborhood. A belief network was applied to detect isolated candidates at a higher sensitivity than clustered ones. The detection performances of the context-sensitive CAD and a conventional CAD were compared by use of leave-one-patient-out evaluation on 73 patients. Conventional CAD detected 82% of the lesions 6 - 9 mm in size with a median of 6 false positives per CT scan, whereas context-sensitive CAD detected the lesions at a median of 4 false positives with significant increment in overall detection performance. For lesions ≥10 mm in size, the detection sensitivity was 98% with a median of 7 false positives per patient, but the improvement in detection performance was not significant.

Probabilistic method for context-sensitive detection of polyps in CT colonography