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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.08534 |
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| _version_ | 1866912422505218048 |
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| author | Li, Donglian Guo, Hui Chen, Minglang Chen, Huizhen Chen, Jialing Liang, Bocheng Liang, Pengchen Tan, Ying |
| author_facet | Li, Donglian Guo, Hui Chen, Minglang Chen, Huizhen Chen, Jialing Liang, Bocheng Liang, Pengchen Tan, Ying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View Li, Donglian Guo, Hui Chen, Minglang Chen, Huizhen Chen, Jialing Liang, Bocheng Liang, Pengchen Tan, Ying Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.08534 |