Novel Methodology to Evaluate Renal Cysts in Polycystic Kidney Disease

Bae, Kyongtae T.; Sun, Hongzan; Lee, June Goo; Bae, Kyungsoo; Wang, Jinhong; Tao, Cheng; Chapman, Arlene B.; Torres, Vicente E.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug

doi:10.1159/000358604

Cited by 15 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm developed in this study accurately segmented renal cysts from kidney tissue without user intervention. Prior to this model, approaches to delineate cystic structures from organ tissue implemented semi-automated intensity-based thresholding techniques [16,17,20]. One limitation of intensity-based approaches is that, unlike CT, MR pixel values can drastically vary between acquisitions, and even between slices within one acquisition, requiring extensive preprocessing techniques to appropriately normalize the data [26].…”

Section: Discussionmentioning

confidence: 99%

“…One limitation of intensity-based approaches is that, unlike CT, MR pixel values can drastically vary between acquisitions, and even between slices within one acquisition, requiring extensive preprocessing techniques to appropriately normalize the data [26]. Furthermore, this technique of intensity-based thresholding will completely miss complex cysts that have lower signal intensity [16].…”

Section: Discussionmentioning

confidence: 99%

“…In the past, cyst and kidney regions have been segmented manually, which is highly labor intensive and subjective [ 15 ]. Various semi-automated cyst segmentation approaches have been proposed using intensity-based thresholding as an initialization [ 16 , 17 ] as well as classical machine learning techniques such as k -means clustering [ 18 ], contour methods [ 19 ], and shape prior probability maps [ 20 ]. However, a fully automated deep learning approach using neural networks has the potential to rid the image analyst from the tedium of manual tracing and provide reproducible and robust volume calculations and segmentations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease

et al. 2020

View full text Add to dashboard Cite

Purpose For patients affected by autosomal-dominant polycystic kidney disease (ADPKD), successful differentiation of cysts is useful for automatic classification of patient phenotypes, clinical decision-making, and disease progression. The objective was to develop and evaluate a fully automated semantic segmentation method to differentiate and analyze renal cysts in patients with ADPKD. Methods An automated deep learning approach using a convolutional neural network was trained, validated, and tested on a set of 60 MR T2-weighted images. A three-fold cross-validation approach was used to train three models on distinct training and validation sets (n = 40). An ensemble model was then built and tested on the hold out cases (n = 20), with each of the cases compared to manual segmentations performed by two readers. Segmentation agreement between readers and the automated method was assessed. Results The automated approach was found to perform at the level of interobserver variability. The automated approach had a Dice coefficient (mean ± standard deviation) of 0.86 ± 0.10 vs Reader-1 and 0.84 ± 0.11 vs. Reader-2. Interobserver Dice was 0.86 ± 0.08. In terms of total cyst volume (TCV), the automated approach had a percent difference of 3.9 ± 19.1% vs Reader-1 and 8.0 ± 24.1% vs Reader-2, whereas interobserver variability was − 2.0 ± 16.4%. Conclusion This study developed and validated a fully automated approach for performing semantic segmentation of kidney cysts in MR images of patients affected by ADPKD. This approach will be useful for exploring additional imaging biomarkers of ADPKD and automatically classifying phenotypes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Since such conditions inhibit automated segmentation renal cysts, manual pre-segmentation of the renal region was required in the current software to separate the liver and kidney regions roughly. This process may be replaced with automated segmentation on the prior statistical information of kidney location 22 for the further application. In addition, the manual contrast adjustment was required in the present study.…”

Section: Discussion the Binarization Process For Mr Imagementioning

confidence: 99%

Skeletonization of Renal Cysts of Autosomal Dominant Polycystic Kidney Disease Using Magnetic Resonance Imaging

Teranaka¹,

Ishii²,

Matsunaga³

et al. 2017

j med imaging hlth inform

View full text Add to dashboard Cite

Autosomal dominant polycystic kidney disease (ADPKD) is a condition in which numerous cysts develop in the renal tubules and grows over the lifetime, resulting in compression of renal parenchyma and worsens renal function. Conventionally, total kidney volume (TKV) is bluntly estimated as a time cross sectional parameter of disease status. In assumption that cyst initiation rate and growth speed of cysts would be another prognostic marker for renal function, and regulate the morphological feature of the enlarged kidney, we attempted to extract a morphological feature of the renal cystic region quantitatively from MRI T2-weighted images. Skeletonization algorithm was applied to the binarized cystic region after extracting cystic regions by discriminant analysis. Then, morphological feature of the renal cystic region was converted to distribution pattern in number and size of cysts, and "branch" of adjacent cysts. The number of "branches" corresponded with the number of cysts, and the cumulative probability curves of "branch" length shifted according to cyst size distribution. The proposed method successfully quantified morphological feature of cystic region objectively in semi-automatic manner. The method would contribute to manage ADPKD patients in deciding time to start therapies after affirmation for consistency with cyst initiation rate, growth speed of cysts and renal function.

show abstract

“…For imaging, issues of access, speed, and accuracy of analysis 25 , 26 as well as harmonisation of accepted standards at a national and international level will be needed. For genotype-based scoring, further refinement of the ‘strength’ of non-truncating mutations through bioinformatics 15 , family studies 14 , or experimental approaches 27 , 28 could increase its predictive value.…”

Section: Prognostic Prediction and Scoresmentioning

confidence: 99%

Autosomal dominant polycystic kidney disease: recent advances in clinical management

2016

View full text Add to dashboard Cite

The first clinical descriptions of autosomal dominant polycystic kidney disease (ADPKD) go back at least 500 years to the late 16 th century. Advances in understanding disease presentation and pathophysiology have mirrored the progress of clinical medicine in anatomy, pathology, physiology, cell biology, and genetics. The identification of PKD1 and PKD2, the major genes mutated in ADPKD, has stimulated major advances, which in turn have led to the first approved drug for this disorder and a fresh reassessment of patient management in the 21 st century. In this commentary, we consider how clinical management is likely to change in the coming decade.

show abstract

Novel Methodology to Evaluate Renal Cysts in Polycystic Kidney Disease

Cited by 15 publications

References 19 publications

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease

Skeletonization of Renal Cysts of Autosomal Dominant Polycystic Kidney Disease Using Magnetic Resonance Imaging

Autosomal dominant polycystic kidney disease: recent advances in clinical management

Contact Info

Product

Resources

About