title: Computer-aided detection of exophytic renal lesions on non-contrast CT images.
abstract: 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.
authors.affiliation: Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
authors.forename: Jianfei
authors.initials: J
authors.lastname: Liu
authors.affiliation: Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
authors.forename: Shijun
authors.initials: S
authors.lastname: Wang
authors.affiliation: Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, USA; Departments of Radiology and Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington DC, USA.
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
authors.affiliation: Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.affiliation: Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA. Electronic address: rms@nih.gov.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003287:Contrast Media; D003951:Diagnostic Errors / Q000517:prevention & control*; D006801:Humans; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media
keywords: Belief propagation; Computer aided diagnosis; Extracolonic findings; Manifold diffusion process; Renal lesions