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nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation
Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical image segmentation tasks. However, the design of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604839/ https://www.ncbi.nlm.nih.gov/pubmed/36295005 http://dx.doi.org/10.3390/life12101570 |
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author | Zhao, Li Zhou, Dongming Jin, Xin Zhu, Weina |
author_facet | Zhao, Li Zhou, Dongming Jin, Xin Zhu, Weina |
author_sort | Zhao, Li |
collection | PubMed |
description | Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical image segmentation tasks. However, the design of segmentation algorithms tends to have more focus on deepening the network architectures and tuning the parameters and hyperparameters manually, which not only leads to a high time and effort consumption, but also causes the problem that the architectures and setting designed for a single task only performs well in a single dataset, but have low performance in other cases. In this paper, nn-TransUNet, an automatic deep learning pipeline for MRI segmentation of the heart is proposed to combine the experiment planning of nnU-net and the network architecture of TransUNet. nn-TransUNet uses vision transformers and convolution layers in the design of the encoder and takes up convolution layers as decoder. With the adaptive preprocessing and network training plan generated by the proposed automatic experiment planning pipeline, nn-TransUNet is able to fulfill the target of medical image segmentation in heart MRI tasks. nn-TransUNet achieved state-of-the-art level in heart MRI segmentation task on Automatic Cardiac Diagnosis Challenge (ACDC) Dataset. It also saves the effort and time to manually tune the parameters and hyperparameters, which can reduce the burden on researchers. |
format | Online Article Text |
id | pubmed-9604839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96048392022-10-27 nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation Zhao, Li Zhou, Dongming Jin, Xin Zhu, Weina Life (Basel) Article Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical image segmentation tasks. However, the design of segmentation algorithms tends to have more focus on deepening the network architectures and tuning the parameters and hyperparameters manually, which not only leads to a high time and effort consumption, but also causes the problem that the architectures and setting designed for a single task only performs well in a single dataset, but have low performance in other cases. In this paper, nn-TransUNet, an automatic deep learning pipeline for MRI segmentation of the heart is proposed to combine the experiment planning of nnU-net and the network architecture of TransUNet. nn-TransUNet uses vision transformers and convolution layers in the design of the encoder and takes up convolution layers as decoder. With the adaptive preprocessing and network training plan generated by the proposed automatic experiment planning pipeline, nn-TransUNet is able to fulfill the target of medical image segmentation in heart MRI tasks. nn-TransUNet achieved state-of-the-art level in heart MRI segmentation task on Automatic Cardiac Diagnosis Challenge (ACDC) Dataset. It also saves the effort and time to manually tune the parameters and hyperparameters, which can reduce the burden on researchers. MDPI 2022-10-09 /pmc/articles/PMC9604839/ /pubmed/36295005 http://dx.doi.org/10.3390/life12101570 Text en © 2022 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 | Article Zhao, Li Zhou, Dongming Jin, Xin Zhu, Weina nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title | nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title_full | nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title_fullStr | nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title_full_unstemmed | nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title_short | nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation |
title_sort | nn-transunet: an automatic deep learning pipeline for heart mri segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604839/ https://www.ncbi.nlm.nih.gov/pubmed/36295005 http://dx.doi.org/10.3390/life12101570 |
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