轻量级网络精准分割胎儿侧脑室，助力产前超声自动诊断
MVSegNet: A Lightweight Boundary-Aware Network for Fetal Lateral Ventricle Segmentation and Atrial Width Estimation in Prenatal Ultrasound
- 融合多尺度特征与边界感知优化，提升弱对比图像分割精度
- 边界和测量误差均优于6种基线模型，宽度估计误差仅3.40毫米
- 参数量仅231万，每秒处理165帧，适合临床实时应用
胎儿侧脑室扩大通过产前超声测量侧脑室体部宽度评估。准确分割是测量关键，但声影、斑点噪声和低对比度带来挑战。我们提出MVSegNet，一种轻量级编码器-解码器网络，结合多尺度特征提取与边界感知优化。模型在584帧经专家标注的经侧脑室超声图像上训练与评估，采用70/15/15划分。性能对比六种分割基线，使用重叠率、边界与测量指标。MVSegNet取得Dice分数80.79%、IoU 68.47%、Hausdorff距离4.07毫米，以及宽度平均绝对误差3.40毫米。模型含231万参数，在NVIDIA T4 GPU上运行速度达165.6帧/秒。该模型在边界与测量指标上超越所有基线，同时保持低计算开销，支持自动化胎儿超声分析。
原文摘要 · Abstract (English)
Fetal ventriculomegaly is assessed by measuring the atrial width of the lateral ventricle in prenatal ultrasound. Accurate segmentation is essential for this measurement, but acoustic shadowing, speckle noise, and poor contrast make it difficult. We developed MVSegNet, a lightweight encoder-decoder network combining multi-scale feature extraction and boundary-aware refinement. The model was trained and evaluated on 584 expert-annotated transventricular ultrasound frames using a 70/15/15 split. Performance was compared against six segmentation baselines using overlap, boundary, and measurement metrics. MVSegNet achieved a Dice score of 80.79%, IoU of 68.47%, Hausdorff distance of 4.07 mm, and atrial width mean absolute error of 3.40 mm. The model contains 2.31 million parameters and runs at 165.6 frames per second on an NVIDIA T4 GPU. MVSegNet outperformed all evaluated baselines on boundary and measurement metrics while maintaining low computational cost, supporting its use in automated fetal ultrasound analysis.
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