title: Manifold diffusion for exophytic kidney lesion detection on non-contrast CT images.
abstract: Kidney lesions are important extracolonic findings at computed tomographic colonography (CTC). However, kidney lesion detection on non-contrast CTC images poses significant challenges due to low image contrast with surrounding tissues. In this paper, we treat the kidney surface as manifolds in Riemannian space and present an intrinsic manifold diffusion approach to identify lesion-caused protrusion while simultaneously removing geometrical noise on the manifolds. Exophytic lesions (those that deform the kidney surface) are detected by searching for surface points with local maximum diffusion response and using the normalized cut algorithm to extract them. Moreover, multi-scale diffusion response is a discriminative feature descriptor for the subsequent classification to reduce false positives: We validated the proposed method and compared it with a baseline method using shape index on CTC datasets from 49 patients. Free-response receiver operating characteristic analysis showed that at 7 false positives, the proposed method achieved 87% sensitivity while the baseline method achieved only 22% sensitivity. The proposed method showed far fewer false positives compared with the baseline method which makes it feasible for clinical practice.
authors: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Science, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors: Jianfei
authors: J
authors: Liu
authors: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Science, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors: Shijun
authors: S
authors: Wang
authors: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Science, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors: Jianhua
authors: J
authors: Yao
authors: Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Medical Center, Washington, DC 20010, USA.
authors: Marius George
authors: MG
authors: Linguraru
authors: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Science, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors: Ronald M
authors: RM
authors: Summers
mesh_terms: D000465:Algorithms; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D033162:Incidental Findings; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity