Automatic classification of heart failure based on Cine-CMR images
© 2023. CARS..
PURPOSE: Heart failure (HF) is a serious and complex syndrome with a high mortality rate. In clinical diagnosis, the correct classification of HF is helpful. In our previous work, we proposed a self-supervised learning framework of HF classification (SSLHF) on cine cardiac magnetic resonance images (Cine-CMR). However, this method lacks the integration of three dimensions of spatial information and temporal information. Thus, this study aims at proposing an automatic 4D HF classification algorithm.
METHODS: To construct a 4D classification model, we proposed an extensional framework called 4D-SSLHF. It mainly consists of self-supervised image restoration and HF classification. The image restoration proxy task utilizes three image transformation methods to enhance the exploration of spatial and temporal information in the Cine-CMR. In the classification task, we proposed a Siamese Conv-LSTM network by combining the Siamese network and bi-directional Conv-LSTM to integrate the features of the four dimensions simultaneously.
RESULTS: Experimental results on 184 patients from Shanghai Chest Hospital achieved an AUC of 0.8794 and an ACC of 0.8402 in the five-fold cross-validation. Compared with our previous work, the improvements in AUC and ACC were 2.89 % and 1.94 %, respectively.
CONCLUSIONS: In this study, we proposed a novel self-supervised learning framework named 4D-SSLHF for HF classification based on Cine-CMR. The proposed 4D-SSLHF can mine 3D spatial information and temporal information in Cine-CMR images well and accurately classify different categories of HF. The good classification results show our method's potential to assist physicians in choosing personalized treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
International journal of computer assisted radiology and surgery - 19(2024), 2 vom: 21. Feb., Seite 355-365 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xie, Yuan [VerfasserIn] |
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| Links: |
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| Themen: |
Automatic classification |
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| Anmerkungen: |
Date Completed 06.02.2024 Date Revised 06.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11548-023-03028-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364138181 |
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| 100 | 1 | |a Xie, Yuan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic classification of heart failure based on Cine-CMR images |
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| 500 | |a Date Revised 06.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. CARS. | ||
| 520 | |a PURPOSE: Heart failure (HF) is a serious and complex syndrome with a high mortality rate. In clinical diagnosis, the correct classification of HF is helpful. In our previous work, we proposed a self-supervised learning framework of HF classification (SSLHF) on cine cardiac magnetic resonance images (Cine-CMR). However, this method lacks the integration of three dimensions of spatial information and temporal information. Thus, this study aims at proposing an automatic 4D HF classification algorithm | ||
| 520 | |a METHODS: To construct a 4D classification model, we proposed an extensional framework called 4D-SSLHF. It mainly consists of self-supervised image restoration and HF classification. The image restoration proxy task utilizes three image transformation methods to enhance the exploration of spatial and temporal information in the Cine-CMR. In the classification task, we proposed a Siamese Conv-LSTM network by combining the Siamese network and bi-directional Conv-LSTM to integrate the features of the four dimensions simultaneously | ||
| 520 | |a RESULTS: Experimental results on 184 patients from Shanghai Chest Hospital achieved an AUC of 0.8794 and an ACC of 0.8402 in the five-fold cross-validation. Compared with our previous work, the improvements in AUC and ACC were 2.89 % and 1.94 %, respectively | ||
| 520 | |a CONCLUSIONS: In this study, we proposed a novel self-supervised learning framework named 4D-SSLHF for HF classification based on Cine-CMR. The proposed 4D-SSLHF can mine 3D spatial information and temporal information in Cine-CMR images well and accurately classify different categories of HF. The good classification results show our method's potential to assist physicians in choosing personalized treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Automatic classification | |
| 650 | 4 | |a Cine-CMR | |
| 650 | 4 | |a Heart failure | |
| 650 | 4 | |a Self-supervised learning | |
| 700 | 1 | |a Zhong, Hai |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Jiaqi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Wangyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Runping |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Jun |e verfasserin |4 aut | |
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