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Published on: November 30, 2022
Fully Automated Segmentation of Polycystic Kidneys From Noncontrast Computed Tomography: A Feasibility Study and
Dario Turco1, Maddalena Valinoti1, Eva Maria Martin2
1Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy, Via Venezia 52, 47521 Cesena, Italy.
Rationale And Objectives:
Total 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 Methods:
Non-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.
Results:
Kidney 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).
Conclusions:
This 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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