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A novel deep learning segmentation model for organoid-based drug screening
Organoids are self-organized three-dimensional in vitro cell cultures derived from stem cells. They can recapitulate organ development, tissue regeneration, and disease progression and, hence, have broad applications in drug discovery. However, the lack of effective graphic algorithms for organoid g...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794595/ https://www.ncbi.nlm.nih.gov/pubmed/36588731 http://dx.doi.org/10.3389/fphar.2022.1080273 |
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author | Wang, Xiaowen Wu, Chunyue Zhang, Shudi Yu, Pengfei Li, Lu Guo, Chunming Li, Rui |
author_facet | Wang, Xiaowen Wu, Chunyue Zhang, Shudi Yu, Pengfei Li, Lu Guo, Chunming Li, Rui |
author_sort | Wang, Xiaowen |
collection | PubMed |
description | Organoids are self-organized three-dimensional in vitro cell cultures derived from stem cells. They can recapitulate organ development, tissue regeneration, and disease progression and, hence, have broad applications in drug discovery. However, the lack of effective graphic algorithms for organoid growth analysis has slowed the development of organoid-based drug screening. In this study, we take advantage of a bladder cancer organoid system and develop a deep learning model, the res-double dynamic conv attention U-Net (RDAU-Net) model, to improve the efficiency and accuracy of organoid-based drug screenings. In this RDAU-Net model, the dynamic convolution and attention modules are integrated. The feature-extracting capability of the encoder and the utilization of multi-scale information are substantially enhanced, and the semantic gap caused by skip connections has been filled, which substantially improved its anti-interference ability. A total of 200 images of bladder cancer organoids on culture days 1, 3, 5, and 7, with or without drug treatment, were employed for training and testing. Compared with the other variations of the U-Net model, the segmentation indicators, such as Intersection over Union and dice similarity coefficient, in the RDAU-Net model have been improved. In addition, this algorithm effectively prevented false identification and missing identification, while maintaining a smooth edge contour of segmentation results. In summary, we proposed a novel method based on a deep learning model which could significantly improve the efficiency and accuracy of high-throughput drug screening and evaluation using organoids. |
format | Online Article Text |
id | pubmed-9794595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97945952022-12-29 A novel deep learning segmentation model for organoid-based drug screening Wang, Xiaowen Wu, Chunyue Zhang, Shudi Yu, Pengfei Li, Lu Guo, Chunming Li, Rui Front Pharmacol Pharmacology Organoids are self-organized three-dimensional in vitro cell cultures derived from stem cells. They can recapitulate organ development, tissue regeneration, and disease progression and, hence, have broad applications in drug discovery. However, the lack of effective graphic algorithms for organoid growth analysis has slowed the development of organoid-based drug screening. In this study, we take advantage of a bladder cancer organoid system and develop a deep learning model, the res-double dynamic conv attention U-Net (RDAU-Net) model, to improve the efficiency and accuracy of organoid-based drug screenings. In this RDAU-Net model, the dynamic convolution and attention modules are integrated. The feature-extracting capability of the encoder and the utilization of multi-scale information are substantially enhanced, and the semantic gap caused by skip connections has been filled, which substantially improved its anti-interference ability. A total of 200 images of bladder cancer organoids on culture days 1, 3, 5, and 7, with or without drug treatment, were employed for training and testing. Compared with the other variations of the U-Net model, the segmentation indicators, such as Intersection over Union and dice similarity coefficient, in the RDAU-Net model have been improved. In addition, this algorithm effectively prevented false identification and missing identification, while maintaining a smooth edge contour of segmentation results. In summary, we proposed a novel method based on a deep learning model which could significantly improve the efficiency and accuracy of high-throughput drug screening and evaluation using organoids. Frontiers Media S.A. 2022-12-14 /pmc/articles/PMC9794595/ /pubmed/36588731 http://dx.doi.org/10.3389/fphar.2022.1080273 Text en Copyright © 2022 Wang, Wu, Zhang, Yu, Li, Guo and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Wang, Xiaowen Wu, Chunyue Zhang, Shudi Yu, Pengfei Li, Lu Guo, Chunming Li, Rui A novel deep learning segmentation model for organoid-based drug screening |
title | A novel deep learning segmentation model for organoid-based drug screening |
title_full | A novel deep learning segmentation model for organoid-based drug screening |
title_fullStr | A novel deep learning segmentation model for organoid-based drug screening |
title_full_unstemmed | A novel deep learning segmentation model for organoid-based drug screening |
title_short | A novel deep learning segmentation model for organoid-based drug screening |
title_sort | novel deep learning segmentation model for organoid-based drug screening |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794595/ https://www.ncbi.nlm.nih.gov/pubmed/36588731 http://dx.doi.org/10.3389/fphar.2022.1080273 |
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