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DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View

10 June 2025
Donglian Li
Hui Guo
Minglang Chen
Huizhen Chen
Jialing Chen
Bocheng Liang
Pengchen Liang
Ying Tan
ArXiv (abs)PDFHTML
Main:11 Pages
3 Figures
Bibliography:3 Pages
4 Tables
Abstract

Accurate segmentation of anatomical structures in the apical four-chamber (A4C) view of fetal echocardiography is essential for early diagnosis and prenatal evaluation of congenital heart disease (CHD). However, precise segmentation remains challenging due to ultrasound artifacts, speckle noise, anatomical variability, and boundary ambiguity across different gestational stages. To reduce the workload of sonographers and enhance segmentation accuracy, we propose DCD, an advanced deep learning-based model for automatic segmentation of key anatomical structures in the fetal A4C view. Our model incorporates a Dense Atrous Spatial Pyramid Pooling (Dense ASPP) module, enabling superior multi-scale feature extraction, and a Convolutional Block Attention Module (CBAM) to enhance adaptive feature representation. By effectively capturing both local and global contextual information, DCD achieves precise and robust segmentation, contributing to improved prenatal cardiac assessment.

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@article{li2025_2506.08534,
  title={ DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View },
  author={ Donglian Li and Hui Guo and Minglang Chen and Huizhen Chen and Jialing Chen and Bocheng Liang and Pengchen Liang and Ying Tan },
  journal={arXiv preprint arXiv:2506.08534},
  year={ 2025 }
}
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