Dataset Information

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study.

ABSTRACT:

Background

Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting.

Methods

The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model's performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients.

Results

The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92-0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996-0.999) with low bias and high precision (-0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of -0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of -1%±7%.

Conclusions

Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible.Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521.

SUBMITTER: Woznicki P

PROVIDER: S-EPMC9802567 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study.

Woznicki Piotr P Siedek Florian F van Gastel Maatje D A MDA Dos Santos Daniel Pinto DP Arjune Sita S Karner Larina A LA Meyer Franziska F Caldeira Liliana Lourenco LL Persigehl Thorsten T Gansevoort Ron T RT Grundmann Franziska F Baessler Bettina B Müller Roman-Ulrich RU

Kidney360 20221229 12

<h4>Background</h4>Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting.<h4>Methods</h4 ...[more]

PMID: 36591351

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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.

Project description:Background & aimsPolycystic liver disease (PLD), the most common extrarenal manifestation of autosomal-dominant polycystic kidney disease (ADPKD), has become more prevalent as a result of increased life expectancy, improved renal survival, reduced cardiovascular mortality, and renal replacement therapy. No studies have fully characterized PLD in large cohorts. We investigated whether liver and cyst volumes are associated with volume of the hepatic parenchyma, results from liver laboratory tests, and patient-reported outcomes.MethodsWe performed a cross-sectional analysis of baseline liver volumes, measured by magnetic resonance imaging, and their association with demographics, results from liver laboratory and other tests, and quality of life. The data were collected from a randomized, placebo-controlled trial underway at 7 tertiary-care medical centers to determine whether the combination of an angiotensin I-converting enzyme inhibitor and angiotensin II-receptor blocker was superior to the inhibitor alone, and whether low blood pressure (<110/75 mm Hg) was superior to standard blood pressure (120-130/70-80 mm Hg), in delaying renal cystic progression in 558 patients with ADPKD, stages 1 and 2 chronic kidney disease, and hypertension (age, 15-49 y).ResultsWe found hepatomegaly to be common among patients with ADPKD. Cysts and parenchyma contributed to hepatomegaly. Cysts were more common and liver and cyst volumes were greater in women, increasing with age. Patients with advanced disease had a relative loss of liver parenchyma. We observed small abnormalities in results from liver laboratory tests, and that splenomegaly and hypersplenism were associated with PLD severity. Higher liver volumes were associated with a lower quality of life.ConclusionsHepatomegaly is common even in early stage ADPKD and is not accounted for by cysts alone. Parenchymal volumes were larger, compared with liver volumes of patients without ADPKD or with those predicted by standardized equations, even among patients without cysts. The severity of PLD was associated with altered biochemical and hematologic features, as well as quality of life. ClinicalTrials.gov identifier: NCT00283686.

Project description:Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.

Project description:Background and objectivesThe progression of polycystic liver disease is not well understood. The purpose of the study is to evaluate the associations of polycystic liver progression with other disease progression variables and classify liver progression on the basis of patient's age, height-adjusted liver cystic volume, and height-adjusted liver volume.Design, setting, participants, & measurementsProspective longitudinal magnetic resonance images from 670 patients with early autosomal dominant polycystic kidney disease for up to 14 years of follow-up were evaluated to measure height-adjusted liver cystic volume and height-adjusted liver volume. Among them, 245 patients with liver cyst volume >50 ml at baseline were included in the longitudinal analysis. Linear mixed models on log-transformed height-adjusted liver cystic volume and height-adjusted liver volume were fitted to approximate mean annual rate of change for each outcome. The association of sex, body mass index, genotype, baseline height-adjusted total kidney volume, and Mayo imaging class was assessed. We calculated height-adjusted liver cystic volume ranges for each specific age and divided them into five classes on the basis of annual percentage increase in height-adjusted liver cystic volume.ResultsThe mean annual growth rate of height-adjusted liver cystic volume was 12% (95% confidence interval, 11.1% to 13.1%; P<0.001), whereas that for height-adjusted liver volume was 2% (95% confidence interval, 1.9% to 2.6%; P<0.001). Women had higher baseline height-adjusted liver cystic volume than men, but men had higher height-adjusted liver cystic volume growth rate than women by 2% (95% confidence interval, 0.4% to 4.5%; P=0.02). Whereas the height-adjusted liver cystic volume growth rate decreased in women after menopause, no decrease was observed in men at any age. Body mass index, genotype, and baseline height-adjusted total kidney volume were not associated with the growth rate of height-adjusted liver cystic volume or height-adjusted liver volume. According to the height-adjusted liver cystic volume growth rate, patients were classified into five classes (number of women, men in each class): A (24, six); B (44, 13); C (43, 48); D (28, 17); and E (13, nine).ConclusionsCompared with height-adjusted liver volume, the use of height-adjusted liver cystic volume showed greater separations in volumetric progression of polycystic liver disease. Similar to the Mayo imaging classification for the kidney, the progression of polycystic liver disease may be categorized on the basis of patient's age and height-adjusted liver cystic volume.

Dataset Information

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study.

Background

Methods

Results

Conclusions

Publications

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study.

Similar Datasets

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