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Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach
The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749636/ https://www.ncbi.nlm.nih.gov/pubmed/35009788 http://dx.doi.org/10.3390/s22010245 |
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author | Oh, Seok Kim, Young-Jae Park, Young-Taek Kim, Kwang-Gi |
author_facet | Oh, Seok Kim, Young-Jae Park, Young-Taek Kim, Kwang-Gi |
author_sort | Oh, Seok |
collection | PubMed |
description | The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images. |
format | Online Article Text |
id | pubmed-8749636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87496362022-01-12 Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach Oh, Seok Kim, Young-Jae Park, Young-Taek Kim, Kwang-Gi Sensors (Basel) Communication The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images. MDPI 2021-12-30 /pmc/articles/PMC8749636/ /pubmed/35009788 http://dx.doi.org/10.3390/s22010245 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Oh, Seok Kim, Young-Jae Park, Young-Taek Kim, Kwang-Gi Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title_full | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title_fullStr | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title_full_unstemmed | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title_short | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach |
title_sort | automatic pancreatic cyst lesion segmentation on eus images using a deep-learning approach |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749636/ https://www.ncbi.nlm.nih.gov/pubmed/35009788 http://dx.doi.org/10.3390/s22010245 |
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