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Fully Automated Segmentation of Polycystic Kidneys From Noncontrast Computed Tomography: A Feasibility Study and Preliminary Results.

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.

journal_name

Acad Radiol

journal_title

Academic radiology

authors

Turco D,Valinoti M,Martin EM,Tagliaferri C,Scolari F,Corsi C

doi

10.1016/j.acra.2017.11.015

subject

Has Abstract

pub_date

2018-07-01 00:00:00

pages

850-855

issue

7

eissn

1076-6332

issn

1878-4046

pii

S1076-6332(17)30502-0

journal_volume

25

pub_type

杂志文章

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