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MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation
Background: In recent years, computer-assisted diagnosis of patients is an increasingly common topic. Multi-organ segmentation of clinical Computed Tomography (CT) images of the patient’s abdomen and magnetic resonance images (MRI) of the patient’s heart is a challenging task in medical image segmen...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704745/ https://www.ncbi.nlm.nih.gov/pubmed/36457896 http://dx.doi.org/10.1109/JTEHM.2022.3210047 |
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collection | PubMed |
description | Background: In recent years, computer-assisted diagnosis of patients is an increasingly common topic. Multi-organ segmentation of clinical Computed Tomography (CT) images of the patient’s abdomen and magnetic resonance images (MRI) of the patient’s heart is a challenging task in medical image segmentation. The accurate segmentation of multiple organs is an important prerequisite for disease diagnosis and treatment planning. Methods: In this paper, we propose a new method based on multi-organ segmentation in CT images or MRI images; this method is based on the CNN-Transformer hybrid model, and on this basis, a progressive sampling module is added. Results: We performed multi-organ segmentation on CT images and MRI images provided by two public datasets, Synapse multi-organ CT dataset (Synapse) and Automated cardiac diagnosis challenge dataset (ACDC). By using Dice Similarity Coefficient (DSC) and Hausdorff_95 (HD95) as the evaluation metric for the Synapse dataset. For the Synapse dataset of CT images, the average DSC reached 79.76%, and the HD95 reached 21.55%. The DSC indicators of Kidney(R), Pancreas, and Stomach reached 80.77%, 59.84%, and 81.11%, respectively. The average DSC for the ACDC dataset of MRI images reaches 91.8%, far exceeding other state-of-the-art techniques. Conclusion: In this paper, we propose a multi-sampled vision transformer MPSHT based on the CNN-Transformer structure. The model has both the advantages of CNN convolutional network and Transformer, and at the same time, the addition of a progressive sampling module makes the model’s segmentation of organs more accurate, making up for the shortcomings of the previous CNN-Transformer hybrid model. |
format | Online Article Text |
id | pubmed-9704745 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-97047452022-11-30 MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation IEEE J Transl Eng Health Med Article Background: In recent years, computer-assisted diagnosis of patients is an increasingly common topic. Multi-organ segmentation of clinical Computed Tomography (CT) images of the patient’s abdomen and magnetic resonance images (MRI) of the patient’s heart is a challenging task in medical image segmentation. The accurate segmentation of multiple organs is an important prerequisite for disease diagnosis and treatment planning. Methods: In this paper, we propose a new method based on multi-organ segmentation in CT images or MRI images; this method is based on the CNN-Transformer hybrid model, and on this basis, a progressive sampling module is added. Results: We performed multi-organ segmentation on CT images and MRI images provided by two public datasets, Synapse multi-organ CT dataset (Synapse) and Automated cardiac diagnosis challenge dataset (ACDC). By using Dice Similarity Coefficient (DSC) and Hausdorff_95 (HD95) as the evaluation metric for the Synapse dataset. For the Synapse dataset of CT images, the average DSC reached 79.76%, and the HD95 reached 21.55%. The DSC indicators of Kidney(R), Pancreas, and Stomach reached 80.77%, 59.84%, and 81.11%, respectively. The average DSC for the ACDC dataset of MRI images reaches 91.8%, far exceeding other state-of-the-art techniques. Conclusion: In this paper, we propose a multi-sampled vision transformer MPSHT based on the CNN-Transformer structure. The model has both the advantages of CNN convolutional network and Transformer, and at the same time, the addition of a progressive sampling module makes the model’s segmentation of organs more accurate, making up for the shortcomings of the previous CNN-Transformer hybrid model. IEEE 2022-09-26 /pmc/articles/PMC9704745/ /pubmed/36457896 http://dx.doi.org/10.1109/JTEHM.2022.3210047 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
spellingShingle | Article MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title | MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title_full | MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title_fullStr | MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title_full_unstemmed | MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title_short | MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation |
title_sort | mpsht: multiple progressive sampling hybrid model multi-organ segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704745/ https://www.ncbi.nlm.nih.gov/pubmed/36457896 http://dx.doi.org/10.1109/JTEHM.2022.3210047 |
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