A novel hybrid layer-based encoder–decoder framework for 3D segmentation in congenital heart disease

The segmentation of cardiac anatomy represents a crucial stage in accurate diagnosis and subsequent treatment planning for patients with congenital heart disease (CHD). However, the current deep learning-based segmentation networks are ineffective when applied to 3D medical images of CHD because of...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 11891 - 10
Hauptverfasser: Zhu, Yaoxi, Li, Hongbo, Cao, Bingxin, Huang, Kun, Liu, Jinping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 07.04.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
3D CT image
639/705
692/308
692/4019
Algorithms
Cardiac segmentation
Cardiovascular disease
Cardiovascular diseases
Congenital diseases
Congenital heart disease
Coronary artery disease
Deep Learning
Heart - diagnostic imaging
Heart Defects, Congenital - diagnostic imaging
Heart diseases
Humanities and Social Sciences
Humans
Hybrid architectures
Image processing
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
multidisciplinary
Science
Science (multidisciplinary)
Deep learning
Hybrid architectures
Congenital heart disease
Cardiac segmentation
3D CT image
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The segmentation of cardiac anatomy represents a crucial stage in accurate diagnosis and subsequent treatment planning for patients with congenital heart disease (CHD). However, the current deep learning-based segmentation networks are ineffective when applied to 3D medical images of CHD because of the limited availability of training datasets and the inherent complexity exhibited by the variability of cardiac and large vessel tissues. To address this challenge, we propose a novel hybrid layer-based encoder–decoder framework for 3D CHD image segmentation. The model incorporates a global volume mixing module and a local volume-based multihead attention module, which uses a self-attention mechanism to explicitly capture the local and global dependencies of the 3D image segmentation process. This enables the model to more effectively learn the shape boundary features of organs, thereby facilitating accurate segmentation of the whole heart (WH) and great vessels. We compare our method with several popular networks on the public ImageCHD and HVSMR-2.0 datasets. The experimental results show that the proposed model achieves excellent performance in WH and great vessel segmentation tasks with high Dice coefficients and IoU indices.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-96251-9