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Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues
Convolutional neural networks (CNNs) have brought hope for the medical image auxiliary diagnosis. However, the shortfall of labeled medical image data is the bottleneck that limits the performance improvement of supervised CNN methods. In addition, annotating a large number of labeled medical image...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717777/ https://www.ncbi.nlm.nih.gov/pubmed/34975444 http://dx.doi.org/10.3389/fninf.2021.782262 |
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author | Liu, Liangliang Zhang, Jing Wang, Jin-xiang Xiong, Shufeng Zhang, Hui |
author_facet | Liu, Liangliang Zhang, Jing Wang, Jin-xiang Xiong, Shufeng Zhang, Hui |
author_sort | Liu, Liangliang |
collection | PubMed |
description | Convolutional neural networks (CNNs) have brought hope for the medical image auxiliary diagnosis. However, the shortfall of labeled medical image data is the bottleneck that limits the performance improvement of supervised CNN methods. In addition, annotating a large number of labeled medical image data is often expensive and time-consuming. In this study, we propose a co-optimization learning network (COL-Net) for Magnetic Resonance Imaging (MRI) segmentation of ischemic penumbra tissues. COL-Net base on the limited labeled samples and consists of an unsupervised reconstruction network (R), a supervised segmentation network (S), and a transfer block (T). The reconstruction network extracts the robust features from reconstructing pseudo unlabeled samples, which is the auxiliary branch of the segmentation network. The segmentation network is used to segment the target lesions under the limited labeled samples and the auxiliary of the reconstruction network. The transfer block is used to co-optimization the feature maps between the bottlenecks of the reconstruction network and segmentation network. We propose a mix loss function to optimize COL-Net. COL-Net is verified on the public ischemic penumbra segmentation challenge (SPES) with two dozen labeled samples. Results demonstrate that COL-Net has high predictive accuracy and generalization with the Dice coefficient of 0.79. The extended experiment also shows COL-Net outperforms most supervised segmentation methods. COL-Net is a meaningful attempt to alleviate the limited labeled sample problem in medical image segmentation. |
format | Online Article Text |
id | pubmed-8717777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87177772021-12-31 Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues Liu, Liangliang Zhang, Jing Wang, Jin-xiang Xiong, Shufeng Zhang, Hui Front Neuroinform Neuroscience Convolutional neural networks (CNNs) have brought hope for the medical image auxiliary diagnosis. However, the shortfall of labeled medical image data is the bottleneck that limits the performance improvement of supervised CNN methods. In addition, annotating a large number of labeled medical image data is often expensive and time-consuming. In this study, we propose a co-optimization learning network (COL-Net) for Magnetic Resonance Imaging (MRI) segmentation of ischemic penumbra tissues. COL-Net base on the limited labeled samples and consists of an unsupervised reconstruction network (R), a supervised segmentation network (S), and a transfer block (T). The reconstruction network extracts the robust features from reconstructing pseudo unlabeled samples, which is the auxiliary branch of the segmentation network. The segmentation network is used to segment the target lesions under the limited labeled samples and the auxiliary of the reconstruction network. The transfer block is used to co-optimization the feature maps between the bottlenecks of the reconstruction network and segmentation network. We propose a mix loss function to optimize COL-Net. COL-Net is verified on the public ischemic penumbra segmentation challenge (SPES) with two dozen labeled samples. Results demonstrate that COL-Net has high predictive accuracy and generalization with the Dice coefficient of 0.79. The extended experiment also shows COL-Net outperforms most supervised segmentation methods. COL-Net is a meaningful attempt to alleviate the limited labeled sample problem in medical image segmentation. Frontiers Media S.A. 2021-12-16 /pmc/articles/PMC8717777/ /pubmed/34975444 http://dx.doi.org/10.3389/fninf.2021.782262 Text en Copyright © 2021 Liu, Zhang, Wang, Xiong and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Liu, Liangliang Zhang, Jing Wang, Jin-xiang Xiong, Shufeng Zhang, Hui Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title | Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title_full | Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title_fullStr | Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title_full_unstemmed | Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title_short | Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues |
title_sort | co-optimization learning network for mri segmentation of ischemic penumbra tissues |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717777/ https://www.ncbi.nlm.nih.gov/pubmed/34975444 http://dx.doi.org/10.3389/fninf.2021.782262 |
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