Fully Automated Segmentation of Polycystic Kidneys From Noncontrast Computed Tomography

Publication date: Available online 10 January 2018
Source:Academic Radiology
Author(s): Dario Turco, Maddalena Valinoti, Eva Maria Martin, Carlo Tagliaferri, Francesco Scolari, Cristiana Corsi
Rationale and ObjectivesTotal kidney volume is an important biomarker for the evaluation of autosomal dominant polycystic kidney disease progression. In this study, we present a novel approach for automated segmentation of polycystic kidneys from non–contrast-enhanced computed tomography (CT) images.Materials and MethodsNon–contrast-enhanced CT images were acquired from 21 patients with a diagnosis of autosomal dominant polycystic kidney disease. Kidney volumes obtained from the fully automated method were compared to volumes obtained by manual segmentation and evaluated using linear regression and Bland-Altman analyses. Dice coefficient was used for performance evaluation.ResultsKidney volumes from the automated method well correlated with the ones obtained by manual segmentation. Bland-Altman analysis showed a low percentage bias (−0.3%) and narrow limits of agreements (11.0%). The overlap between the three-dimensional kidney surfaces obtained with our approach and by manual tracing, expressed in terms of Dice coefficient, showed good agreement (0.91 ± 0.02).ConclusionsThis preliminary study showed the proposed fully automated method for renal volume assessment is feasible, exhibiting how a correct use of biomedical image processing may allow polycystic kidney segmentation also in non–contrast-enhanced CT. Further investigation on a larger dataset is needed to confirm the robustness of the presented approach.



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