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Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease.

ABSTRACT: To evaluate the performance of a semi-automated method for the segmentation of individual renal cysts from magnetic resonance (MR) images in patients with autosomal dominant polycystic kidney disease (ADPKD).This semi-automated method was based on a morphologic watershed technique with shape-detection level set for segmentation of renal cysts from MR images. T2-weighted MR image sets of 40 kidneys were selected from 20 patients with mild to moderate renal cyst burden (kidney volume < 1500 ml) in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP). The performance of the semi-automated method was assessed in terms of two reference metrics in each kidney: the total number of cysts measured by manual counting and the total volume of cysts measured with a region-based thresholding method. The proposed and reference measurements were compared using intraclass correlation coefficient (ICC) and Bland-Altman analysis.Individual renal cysts were successfully segmented with the semi-automated method in all 20 cases. The total number of cysts in each kidney measured with the two methods correlated well (ICC, 0.99), with a very small relative bias (0.3% increase with the semi-automated method; limits of agreement, 15.2% reduction to 17.2% increase). The total volume of cysts measured using both methods also correlated well (ICC, 1.00), with a small relative bias of <10% (9.0% decrease in the semi-automated method; limits of agreement, 17.1% increase to 43.3% decrease).This semi-automated method to segment individual renal cysts in ADPKD kidneys provides a quantitative indicator of severity in early and moderate stages of the disease.

SUBMITTER: Bae K

PROVIDER: S-EPMC3700699 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease.

Bae Kyungsoo K Park Bumwoo B Sun Hongliang H Wang Jinhong J Tao Cheng C Chapman Arlene B AB Torres Vicente E VE Grantham Jared J JJ Mrug Michal M Bennett William M WM Flessner Michael F MF Landsittel Doug P DP Bae Kyongtae T KT

Clinical journal of the American Society of Nephrology : CJASN 20130321 7

<h4>Objective</h4>To evaluate the performance of a semi-automated method for the segmentation of individual renal cysts from magnetic resonance (MR) images in patients with autosomal dominant polycystic kidney disease (ADPKD).<h4>Design, setting, participants, & measurements</h4>This semi-automated method was based on a morphologic watershed technique with shape-detection level set for segmentation of renal cysts from MR images. T2-weighted MR image sets of 40 kidneys were selected from 20 patie ...[more]

PMID: 23520042

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Project description:Background and objectivesOur study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.Design, setting, participants, & measurementsStudy patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.ResultsSuccessful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable.ConclusionsWe have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.

Dataset Information

Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease.

Publications

Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease.

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