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Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules
Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-cons...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952263/ https://www.ncbi.nlm.nih.gov/pubmed/32038870 http://dx.doi.org/10.1049/htl.2019.0086 |
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author | Fernandez, Katrina Korinek, Mark Camp, Jon Lieske, John Holmes, David |
author_facet | Fernandez, Katrina Korinek, Mark Camp, Jon Lieske, John Holmes, David |
author_sort | Fernandez, Katrina |
collection | PubMed |
description | Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-consuming. Deep learning has become a popular technique for semantic segmentation because of the high accuracy that has been demonstrated. The present Letter examined the efficacy of deep learning to segment the renal papilla, plaque, and plugs. A U-Net model with ResNet-34 encoder was tested; the Letter examined dropout (to avoid overtraining) and two different loss functions (to address the class imbalance problem. The models were then trained in 1666 images and tested on 185 images. The Jaccard-cross-entropy loss function was more effective than the focal loss function. The model with the dropout rate 0.4 was found to be more effective due to its generalisability. The model was largely successful at delineating the papilla. The model was able to correctly detect the plaques and plugs; however, small plaques were challenging. Deep learning was found to be applicable for segmentation of an endoscopic image for the papilla, plaque, and plug, with room for improvement. |
format | Online Article Text |
id | pubmed-6952263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-69522632020-02-07 Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules Fernandez, Katrina Korinek, Mark Camp, Jon Lieske, John Holmes, David Healthc Technol Lett Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-consuming. Deep learning has become a popular technique for semantic segmentation because of the high accuracy that has been demonstrated. The present Letter examined the efficacy of deep learning to segment the renal papilla, plaque, and plugs. A U-Net model with ResNet-34 encoder was tested; the Letter examined dropout (to avoid overtraining) and two different loss functions (to address the class imbalance problem. The models were then trained in 1666 images and tested on 185 images. The Jaccard-cross-entropy loss function was more effective than the focal loss function. The model with the dropout rate 0.4 was found to be more effective due to its generalisability. The model was largely successful at delineating the papilla. The model was able to correctly detect the plaques and plugs; however, small plaques were challenging. Deep learning was found to be applicable for segmentation of an endoscopic image for the papilla, plaque, and plug, with room for improvement. The Institution of Engineering and Technology 2019-12-06 /pmc/articles/PMC6952263/ /pubmed/32038870 http://dx.doi.org/10.1049/htl.2019.0086 Text en http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
spellingShingle | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions Fernandez, Katrina Korinek, Mark Camp, Jon Lieske, John Holmes, David Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title | Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title_full | Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title_fullStr | Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title_full_unstemmed | Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title_short | Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
title_sort | automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules |
topic | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952263/ https://www.ncbi.nlm.nih.gov/pubmed/32038870 http://dx.doi.org/10.1049/htl.2019.0086 |
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