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


Journal Article

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-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.

Full Text

Cited Authors

  • Liu, J; Wang, S; Linguraru, MG; Yao, J; Summers, RM

Published Date

  • January 2015

Published In

Volume / Issue

  • 19 / 1

Start / End Page

  • 15 - 29

PubMed ID

  • 25189363

Pubmed Central ID

  • 25189363

Electronic International Standard Serial Number (EISSN)

  • 1361-8423

International Standard Serial Number (ISSN)

  • 1361-8415

Digital Object Identifier (DOI)

  • 10.1016/


  • eng