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Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat
BACKGROUND: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinica...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303341/ https://www.ncbi.nlm.nih.gov/pubmed/37381051 http://dx.doi.org/10.1186/s42826-023-00167-2 |
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author | Hwang, Ji-Hee Lim, Minyoung Han, Gyeongjin Park, Heejin Kim, Yong-Bum Park, Jinseok Jun, Sang-Yeop Lee, Jaeku Cho, Jae-Woo |
author_facet | Hwang, Ji-Hee Lim, Minyoung Han, Gyeongjin Park, Heejin Kim, Yong-Bum Park, Jinseok Jun, Sang-Yeop Lee, Jaeku Cho, Jae-Woo |
author_sort | Hwang, Ji-Hee |
collection | PubMed |
description | BACKGROUND: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3(+), and SSD, to detect hepatic fibrosis. RESULTS: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3(+) also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). CONCLUSIONS: We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies. |
format | Online Article Text |
id | pubmed-10303341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103033412023-06-29 Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat Hwang, Ji-Hee Lim, Minyoung Han, Gyeongjin Park, Heejin Kim, Yong-Bum Park, Jinseok Jun, Sang-Yeop Lee, Jaeku Cho, Jae-Woo Lab Anim Res Research BACKGROUND: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3(+), and SSD, to detect hepatic fibrosis. RESULTS: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3(+) also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). CONCLUSIONS: We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies. BioMed Central 2023-06-28 /pmc/articles/PMC10303341/ /pubmed/37381051 http://dx.doi.org/10.1186/s42826-023-00167-2 Text en © The Author(s) 2023 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, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hwang, Ji-Hee Lim, Minyoung Han, Gyeongjin Park, Heejin Kim, Yong-Bum Park, Jinseok Jun, Sang-Yeop Lee, Jaeku Cho, Jae-Woo Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title | Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title_full | Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title_fullStr | Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title_full_unstemmed | Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title_short | Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat |
title_sort | segmentation algorithm can be used for detecting hepatic fibrosis in sd rat |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303341/ https://www.ncbi.nlm.nih.gov/pubmed/37381051 http://dx.doi.org/10.1186/s42826-023-00167-2 |
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