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Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements
PURPOSE: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert anno...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226108/ https://www.ncbi.nlm.nih.gov/pubmed/35476147 http://dx.doi.org/10.1007/s00261-022-03521-5 |
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author | Jagtap, Jaidip M. Gregory, Adriana V. Homes, Heather L. Wright, Darryl E. Edwards, Marie E. Akkus, Zeynettin Erickson, Bradley J. Kline, Timothy L. |
author_facet | Jagtap, Jaidip M. Gregory, Adriana V. Homes, Heather L. Wright, Darryl E. Edwards, Marie E. Akkus, Zeynettin Erickson, Bradley J. Kline, Timothy L. |
author_sort | Jagtap, Jaidip M. |
collection | PubMed |
description | PURPOSE: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients. METHOD: We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison. RESULTS: Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R(2) = 0.81, and − 4.42%, and between AI and reference standard were R(2) = 0.93, and − 4.12%, respectively. MRI and US measured kidney volumes had R(2) = 0.84 and a bias of 7.47%. CONCLUSION: This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00261-022-03521-5. |
format | Online Article Text |
id | pubmed-9226108 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-92261082022-06-25 Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements Jagtap, Jaidip M. Gregory, Adriana V. Homes, Heather L. Wright, Darryl E. Edwards, Marie E. Akkus, Zeynettin Erickson, Bradley J. Kline, Timothy L. Abdom Radiol (NY) Kidneys, Ureters, Bladder, Retroperitoneum PURPOSE: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients. METHOD: We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison. RESULTS: Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R(2) = 0.81, and − 4.42%, and between AI and reference standard were R(2) = 0.93, and − 4.12%, respectively. MRI and US measured kidney volumes had R(2) = 0.84 and a bias of 7.47%. CONCLUSION: This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00261-022-03521-5. Springer US 2022-04-27 2022 /pmc/articles/PMC9226108/ /pubmed/35476147 http://dx.doi.org/10.1007/s00261-022-03521-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Kidneys, Ureters, Bladder, Retroperitoneum Jagtap, Jaidip M. Gregory, Adriana V. Homes, Heather L. Wright, Darryl E. Edwards, Marie E. Akkus, Zeynettin Erickson, Bradley J. Kline, Timothy L. Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title | Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title_full | Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title_fullStr | Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title_full_unstemmed | Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title_short | Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements |
title_sort | automated measurement of total kidney volume from 3d ultrasound images of patients affected by polycystic kidney disease and comparison to mr measurements |
topic | Kidneys, Ureters, Bladder, Retroperitoneum |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226108/ https://www.ncbi.nlm.nih.gov/pubmed/35476147 http://dx.doi.org/10.1007/s00261-022-03521-5 |
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