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Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning
Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582008/ https://www.ncbi.nlm.nih.gov/pubmed/37848662 http://dx.doi.org/10.1038/s41598-023-44736-w |
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author | Oh, Namkee Kim, Jae-Hun Rhu, Jinsoo Jeong, Woo Kyoung Choi, Gyu-seong Kim, Jong Man Joh, Jae-Won |
author_facet | Oh, Namkee Kim, Jae-Hun Rhu, Jinsoo Jeong, Woo Kyoung Choi, Gyu-seong Kim, Jong Man Joh, Jae-Won |
author_sort | Oh, Namkee |
collection | PubMed |
description | Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon’s standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model’s performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety. |
format | Online Article Text |
id | pubmed-10582008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105820082023-10-19 Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning Oh, Namkee Kim, Jae-Hun Rhu, Jinsoo Jeong, Woo Kyoung Choi, Gyu-seong Kim, Jong Man Joh, Jae-Won Sci Rep Article Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon’s standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model’s performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety. Nature Publishing Group UK 2023-10-17 /pmc/articles/PMC10582008/ /pubmed/37848662 http://dx.doi.org/10.1038/s41598-023-44736-w 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/) . |
spellingShingle | Article Oh, Namkee Kim, Jae-Hun Rhu, Jinsoo Jeong, Woo Kyoung Choi, Gyu-seong Kim, Jong Man Joh, Jae-Won Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title | Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title_full | Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title_fullStr | Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title_full_unstemmed | Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title_short | Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning |
title_sort | automated 3d liver segmentation from hepatobiliary phase mri for enhanced preoperative planning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582008/ https://www.ncbi.nlm.nih.gov/pubmed/37848662 http://dx.doi.org/10.1038/s41598-023-44736-w |
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