CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI
Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system’s structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image...
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Published in | Computerized medical imaging and graphics Vol. 115; p. 102382 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.07.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system’s structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background. The first proposed solution here is to manually segment the regions, which is a time-consuming and error-prone procedure. In this context, many semi- or fully automatic solutions have been proposed recently, among which deep learning-based methods have revealed high performance in segmenting regions in CMRI data. In this study, a self-adaptive multi attention (SMA) module is introduced to adaptively leverage multiple attention mechanisms for better segmentation. The convolutional-based position and channel attention mechanisms with a patch tokenization-based vision transformer (ViT)-based attention mechanism in a hybrid and end-to-end manner are integrated into the SMA. The CNN- and ViT-based attentions mine the short- and long-range dependencies for more precise segmentation. The SMA module is applied in an encoder-decoder structure with a ResNet50 backbone named CardSegNet. Furthermore, a deep supervision method with multi-loss functions is introduced to the CardSegNet optimizer to reduce overfitting and enhance the model’s performance. The proposed model is validated on the ACDC2017 (n=100), M&Ms (n=321), and a local dataset (n=22) using the 10-fold cross-validation method with promising segmentation results, demonstrating its outperformance versus its counterparts.
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•An adaptive attention fusion module is designed in the CardSegNet framework to extract multi-range dependencies efficiently by leveraging pixel-, channel-, and patch-based attentions.•A multi-scale deep supervision method and a joint countor-aware loss function are also incorporated into the optimization of the proposed model.•The model was trained and evaluated on ACDC, M&Ms, and a local dataset, achieving promising Dice scores for LV, Myo, and RV segmentation.•CardSegNet is advantageous for rapid and reliable automated segmentation of the heart region in cardiac MRI data analysis. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0895-6111 1879-0771 1879-0771 |
DOI: | 10.1016/j.compmedimag.2024.102382 |