Article ; Online: Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network.
Magnetic resonance in medicine
2021 Volume 86, Issue 2, Page(s) 1125–1136
Abstract: Purpose: Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T: Methods: This automated method uses machine learning, specifically a 2D ... ...
| Abstract | Purpose: Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T Methods: This automated method uses machine learning, specifically a 2D convolutional neural network (CNN), to accurately segment the left and right kidneys from T Results: The unseen test data processed by the 2D CNN had a mean Dice score of 0.93 ± 0.01. The difference between manual and automatically computed TKV was 1.2 ± 16.2 mL with a mean surface distance of 0.65 ± 0.21 mm. The variance in TKV measurements from repeat acquisitions on the same subject was significantly lower using the automated method compared to manual segmentation of the kidneys. Conclusion: The 2D CNN method provides fully automated segmentation of the left and right kidney and calculation of TKV in <10 s on a standard office computer, allowing high data throughput and is a freely available executable. |
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| MeSH term(s) | Humans ; Image Processing, Computer-Assisted ; Kidney/diagnostic imaging ; Machine Learning ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Renal Insufficiency, Chronic/diagnostic imaging | |||||
| Language | English | |||||
| Publishing date | 2021-03-23 | |||||
| Publishing country | United States | |||||
| Document type | Journal Article ; Research Support, Non-U.S. Gov't | |||||
| ZDB-ID | 605774-3 | |||||
| ISSN | 1522-2594 ; 0740-3194 | |||||
| ISSN (online) | 1522-2594 | |||||
| ISSN | 0740-3194 | |||||
| DOI | 10.1002/mrm.28768 | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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