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Hepatic vessels segmentation using deep learning and preprocessing enhancement

PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS: Recently, the convoluti...

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Autores principales: Alirr, Omar Ibrahim, Rahni, Ashrani Aizzuddin Abd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161019/
https://www.ncbi.nlm.nih.gov/pubmed/36933239
http://dx.doi.org/10.1002/acm2.13966
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author Alirr, Omar Ibrahim
Rahni, Ashrani Aizzuddin Abd
author_facet Alirr, Omar Ibrahim
Rahni, Ashrani Aizzuddin Abd
author_sort Alirr, Omar Ibrahim
collection PubMed
description PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning‐based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U‐net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. RESULTS: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. CONCLUSIONS: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.
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spelling pubmed-101610192023-05-06 Hepatic vessels segmentation using deep learning and preprocessing enhancement Alirr, Omar Ibrahim Rahni, Ashrani Aizzuddin Abd J Appl Clin Med Phys Medical Imaging PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning‐based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U‐net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. RESULTS: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. CONCLUSIONS: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning. John Wiley and Sons Inc. 2023-03-18 /pmc/articles/PMC10161019/ /pubmed/36933239 http://dx.doi.org/10.1002/acm2.13966 Text en © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Imaging
Alirr, Omar Ibrahim
Rahni, Ashrani Aizzuddin Abd
Hepatic vessels segmentation using deep learning and preprocessing enhancement
title Hepatic vessels segmentation using deep learning and preprocessing enhancement
title_full Hepatic vessels segmentation using deep learning and preprocessing enhancement
title_fullStr Hepatic vessels segmentation using deep learning and preprocessing enhancement
title_full_unstemmed Hepatic vessels segmentation using deep learning and preprocessing enhancement
title_short Hepatic vessels segmentation using deep learning and preprocessing enhancement
title_sort hepatic vessels segmentation using deep learning and preprocessing enhancement
topic Medical Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161019/
https://www.ncbi.nlm.nih.gov/pubmed/36933239
http://dx.doi.org/10.1002/acm2.13966
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