用深度学习精准分割胎儿心脏超声四腔图，助力先天性心脏病早筛
DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
- 融合密集空洞金字塔与注意力机制，多尺度提取特征
- 在胎儿超声四腔图上实现高精度结构分割
- 适合医学影像分析与产前诊断研究者参考
胎儿心超的尖端四腔视图（A4C）中解剖结构的精确分割对先天性心脏病（CHD）的早期诊断和产前评估至关重要。然而，由于超声伪影、斑点噪声、解剖变异及不同孕周边界模糊等问题，精确分割仍具挑战。为减轻超声医师工作负担并提升分割精度，本文提出DCD模型，一种基于深度学习的自动分割方法，用于胎儿A4C视图中关键解剖结构的识别。该模型引入密集空洞空间金字塔池化（Dense ASPP）模块以增强多尺度特征提取能力，并结合卷积块注意力模块（CBAM）以优化自适应特征表示。通过有效捕捉局部与全局上下文信息，DCD实现了精准且鲁棒的分割，有助于提升产前心脏评估水平。
原文摘要 · Abstract (English)
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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