DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
Journal:
arXiv
Published Date:
Jun 10, 2025
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.
