Computer-aided detection of exophytic renal lesions on non-contrast CT images

• We developed a novel computer-aided diagnosis system to detect renal lesions on non-contrast CT images.
• We designed an efficient belief propagation method to accurately segment kidneys on non-contrast CT images.
• Manifold diffusion was developed to accurately and smoothly measure shape changes on the kidney surface.
• We detected renal lesions with local maximum diffusion response on the kidney surface graph.
• Manifold diffusion achieved 95% sensitivity with 15 false positives per patient on 167 patients.

Renal lesions are important extracolonic findings on computed tomographic colonography (CTC). They are difficult to detect on non-contrast CTC images due to low image contrast with surrounding objects. In this paper, we developed a novel computer-aided diagnosis system to detect a subset of renal lesions, exophytic lesions, by (1) exploiting efficient belief propagation to segment kidneys, (2) establishing an intrinsic manifold diffusion on kidney surface, (3) searching for potential lesion-caused protrusions with local maximum diffusion response, and (4) exploring novel shape descriptors, including multi-scale diffusion response, with machine learning to classify exophytic renal lesions. Experimental results on the validation dataset with 167 patients revealed that manifold diffusion significantly outperformed conventional shape features (p<1e-3p<1e-3) and resulted in 95% sensitivity with 15 false positives per patient for detecting exophytic renal lesions. Fivefold cross-validation also demonstrated that our method could stably detect exophytic renal lesions. These encouraging results demonstrated that manifold diffusion is a key means to enable accurate computer-aided diagnosis of renal lesions.

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