Dario Turco1, Maddalena Valinoti1, Eva Maria Martin2, Carlo Tagliaferri2, Francesco Scolari3, Cristiana Corsi4. 1. Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy, Via Venezia 52, 47521 Cesena, Italy. 2. Radiology Unit, Spedali Civili Hospital, Brescia, Italy. 3. Nephrology Unit, Spedali Civili Hospital, Brescia, Italy. 4. Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy, Via Venezia 52, 47521 Cesena, Italy. Electronic address: cristiana.corsi3@unibo.it.
Abstract
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.
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.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Adriana V Gregory; Deema A Anaam; Andrew J Vercnocke; Marie E Edwards; Vicente E Torres; Peter C Harris; Bradley J Erickson; Timothy L Kline Journal: J Digit Imaging Date: 2021-04-05 Impact factor: 4.056