Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease.
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ABSTRACT:
SUBMITTER: Sharma K
PROVIDER: S-EPMC5435691 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
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Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease.
Sharma Kanishka K Rupprecht Christian C Caroli Anna A Aparicio Maria Carolina MC Remuzzi Andrea A Baust Maximilian M Navab Nassir N
Scientific reports 20170517 1
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume (TKV) is crucial for studying disease progression in ADPKD. However, automatic segmentation of polycystic kidneys is a challenging task due to severe alteration in the morphology caused by non-uniform cyst formation and presence of adjacent liver cys ...[more]
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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.
| S-EPMC4822671 | biostudies-literature
Project description:BackgroundThe Mayo Clinic Image Classification (MIC) was proposed as a renal prognosis prediction model for autosomal dominant polycystic kidney disease (ADPKD). MIC is based on the assumption of exponential constant increase in height-adjusted total kidney volume (HtTKV). HtTKV growth rate is calculated by one-time measurement of HtTKV and age. We named it as an age-adjusted HtTKV growth rate (AHTKV-?). AHTKV-? was compared with HtTKV slope measured by at least two HtTKV values.MethodsComparison of repeatability between AHTKV-? and HtTKV slope, correlation of subgroups divided according to baseline AHTKV-? and HtTKV slope with disease manifestations, estimated glomerular filtration rate (eGFR) slope, and renal survival were analyzed in 296 patients with ADPKD. PKD genotype influences were compared between AHTKV-? and HtTKV slope in 88 patients with characterized PKD mutations.ResultsAbsolute differences between baseline and follow-up measures were significantly larger for the HtTKV slope than for AHTKV-? (P?<?0.0001). From baseline AHTKV-?-based subgroups A-E according to MIC, disease manifestations occurred earlier and future eGFR slopes became steeper (P?<?0.0001). Multivariate hazard ratios of renal survival differed significantly among baseline AHTKV-?-based subgroups. Inter-subgroup differences in these predictors were less evident during baseline HtTKV slope-based classification. AHTKV-? values, but not HtTKV slopes, were significantly higher for PKD1 mutation carriers than for PKD2 mutation carriers (P?<?0.0001).ConclusionMIC is a good renal prediction model applicable to Japanese patients also. AHTKV-? can be a more sensitive and reliable indicator in TKV growth rate than HtTKV slope.
| S-EPMC6344392 | biostudies-literature
Project description:Autosomal dominant polycystic kidney disease (ADPKD) is characterized by increased total kidney volume (TKV) and renal failure. This study aimed to determine if height-adjusted TKV (htTKV) predicts the onset of renal insufficiency.This prospective, observational, longitudinal, multicenter study included 241 adults with ADPKD and preserved renal function. Magnetic resonance imaging and iothalamate clearance were used to measure htTKV and GFR, respectively. The association between baseline htTKV and the attainment of stage 3 CKD (GFR <60 ml/min per 1.73 m(2)) during follow-up was determined.After a mean follow-up of 7.9 years, stage 3 CKD was attained in 30.7% of the enrollees. Using baseline htTKV, negative correlations with GFR increased from -0.22 at baseline to -0.65 at year 8. In multivariable analysis, a baseline htTKV increase of 100 cc/m significantly predicted the development of CKD within 8 years with an odds ratio of 1.48 (95% confidence interval: 1.29, 1.70). In receiver operator characteristic curve analysis, baseline htTKV of 600 cc/m most accurately defined the risk of developing stage 3 CKD within 8 years with an area under the curve of 0.84 (95% confidence interval: 0.79, 0.90). htTKV was a better predictor than baseline age, serum creatinine, BUN, urinary albumin, or monocyte chemotactic protein-1 excretion (P<0.05).Baseline htTKV ?600 cc/m predicted the risk of developing renal insufficiency in ADPKD patients at high risk for renal disease progression within 8 years of follow-up, qualifying htTKV as a prognostic biomarker in ADPKD.
| S-EPMC3302672 | biostudies-literature
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.
| S-EPMC9326744 | biostudies-literature
Project description:The Consortium of Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) recently showed that renal enlargement in autosomal-dominant polycystic kidney disease mimicked exponential growth. We determined the effects of cyst initiation rate, total number, and growth rate on the time-dependent change of total cyst volume (TCV). Mathematical models with equations integrating cyst surface area, volume, and an invariant growth rate constant were used to compute the time-dependent change in volume of solitary and multiple cysts. Multiple expanding cysts increased TCV in an exponential-like pattern even when individual cysts formed at different rates or exhibited different but constant growth rates. TCV depended on the rate of cyst initiation and on the total number of cysts; however, the compounding effect of exponential-like growth was the most powerful determinant of long-term cyst expansion. Extrapolation of TCV data plots for individual subjects back to an age of 18 predicted TCV values within an established range. We conclude that cysts started early in life were the main contributor to eventual TCV while their growth rate primarily determined renal size; although the rate of formation and the ultimate number of cysts also contributed. The good fit between the exponential models and the extrapolated CRISP data indicates that the TCV growth rate is a defining trait for individual patients and may be used as a prognostic marker.
| S-EPMC2790405 | biostudies-literature
Project description:BackgroundTotal kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.MethodsWe developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T2 -weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland-Altman analysis to assess the performance of the automated segmentation method compared with the manual method.ResultsThe automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of -2.424 ml (95% limits of agreement, -49.80 to 44.95).ConclusionsWe developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.
| S-EPMC9342631 | biostudies-literature
Project description:BackgroundIn autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is regarded as an important biomarker of disease progression and different methods are available to assess kidney volume. The purpose of this study was to identify the most efficient kidney volume computation method to be used in clinical studies evaluating the effectiveness of treatments on ADPKD progression.Methods and findingsWe measured single kidney volume (SKV) on two series of MR and CT images from clinical studies on ADPKD (experimental dataset) by two independent operators (expert and beginner), twice, using all of the available methods: polyline manual tracing (reference method), free-hand manual tracing, semi-automatic tracing, Stereology, Mid-slice and Ellipsoid method. Additionally, the expert operator also measured the kidney length. We compared different methods for reproducibility, accuracy, precision, and time required. In addition, we performed a validation study to evaluate the sensitivity of these methods to detect the between-treatment group difference in TKV change over one year, using MR images from a previous clinical study. Reproducibility was higher on CT than MR for all methods, being highest for manual and semiautomatic contouring methods (planimetry). On MR, planimetry showed highest accuracy and precision, while on CT accuracy and precision of both planimetry and Stereology methods were comparable. Mid-slice and Ellipsoid method, as well as kidney length were fast but provided only a rough estimate of kidney volume. The results of the validation study indicated that planimetry and Stereology allow using an importantly lower number of patients to detect changes in kidney volume induced by drug treatment as compared to other methods.ConclusionsPlanimetry should be preferred over fast and simplified methods for accurately monitoring ADPKD progression and assessing drug treatment effects. Expert operators, especially on MR images, are required for performing reliable estimation of kidney volume. The use of efficient TKV quantification methods considerably reduces the number of patients to enrol in clinical investigations, making them more feasible and significant.
| S-EPMC5448775 | biostudies-literature
Project description:In autosomal dominant polycystic kidney disease (ADPKD), progressive kidney cyst formation commonly leads to ESRD. Because important manifestations of ADPKD may be evident in childhood, early intervention may have the largest effect on long-term outcome. Statins are known to slow progressive nephropathy in animal models of ADPKD. This randomized double-blind placebo-controlled phase III clinical trial was conducted from 2007 to 2012 to assess the effect of pravastatin on height-corrected total kidney volume (HtTKV) and left ventricular mass index (LVMI) by magnetic resonance imaging (MRI) and urine microalbumin excretion (UAE) in children and young adults with ADPKD.There were 110 pediatric participants with ADPKD and normal kidney function receiving lisinopril who were randomized to treatment with pravastatin or placebo for a 3-year period with evaluation at 0, 18, and 36 months. The primary outcome variable was a ? 20% change in HtTKV, LVMI, or UAE over the study period.Ninety-one participants completed the 3-year study (83%). Fewer participants receiving pravastatin achieved the primary endpoint compared with participants receiving placebo (69% versus 88%; P=0.03). This was due primarily to a lower proportion reaching the increase in HtTKV (46% versus 68%; P=0.03), with similar findings observed between study groups for LVMI (25% versus 38%; P=0.18) and UAE (47% versus 39%; P=0.50). The percent change in HtTKV adjusted for age, sex, and hypertension status over the 3-year period was significantly decreased with pravastatin (23% ± 3% versus 31% ± 3%; P=0.02).Pravastatin is an effective agent to slow progression of structural kidney disease in children and young adults with ADPKD. These findings support a role for early intervention with pravastatin in this condition.
| S-EPMC4011448 | biostudies-literature
Project description:PurposeIn ADPKD patients total kidney volume (TKV) measurement using MRI is performed to predict rate of disease progression. Historically T1 weighted images (T1) were used, but the methodology of T2 weighted imaging (T2) has evolved. We compared the performance of both sequences.Methods40 ADPKD patients underwent an abdominal MRI at baseline and follow-up. TKV was measured by manual tracing with Analyze Direct 11.0 software. Three readers established intra- and interreader coefficients of variation (CV). T1 and T2 measured kidney volumes and growth rates were compared with ICC and Bland-Altman analyses.ResultsParticipants were 49.7 ± 7.0 years of age, 55.0% female, with estimated GFR of 50.1 ± 11.5 mL/min/1.73 m2. CVs were low and comparable for T2 and T1 (intrareader: 0.83% [0.48-1.79] vs. 1.15% [0.34-1.77], P = 0.9, interreader: 2.18% [1.59-2.61] vs. 1.69% [1.07-3.87], P = 0.9). TKV was clinically similar, but statistically significantly different between T2 and T1: 1867 [1172-2721] vs. 1932 [1180-2551] mL, respectively (P = 0.006), with a bias of only 0.8% and high agreement (ICC 0.997). Percentage kidney growth during 2.2 ± 0.3 years was similar for T2 and T1 (9.3 ± 10.6% vs. 7.8 ± 9.9%, P = 0.1, respectively), with a bias of 1.5% and high agreement (ICC 0.843). T2 was more often of sufficient quality for volume measurement (86.7% vs. 71.1%, P < 0.001).ConclusionsIn patients with ADPKD, measurement of kidney volume and growth rate performs similarly when using T2 compared to T1 weighted images, although T2 performs better on secondary outcome parameters; they are more often of sufficient quality for volume measurement and result in slightly lower intra- and interreader variability.
| S-EPMC5904223 | biostudies-literature
Project description:ObjectivesTo develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD).MethodsTKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD.ResultsSixty-one patients (mean age 45?±?14 years, baseline eGFR 76?±?32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5?±?5.3% for right kidney, -?0.7?±?5.5% for left kidney), reproducible (intra-operator variability -?0.2?±?1.3%; inter-operator variability 1.1?±?2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45?±?3.96%, n?=?65).ConclusionsThe Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice.Key points• This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.
| S-EPMC6610271 | biostudies-literature