Abstract
Automated segmentation of the fetal head in ultrasound images is critical for prenatal monitoring. However, achieving robust segmentation remains challenging due to the poor quality of ultrasound images and the lack of annotated data. Semi-supervised methods alleviate the lack of annotated data but struggle with the unique characteristics of fetal head ultrasound images, making it challenging to generate reliable pseudo-labels and enforce effective consistency regularization constraints. To address this issue, we propose a novel semi-supervised framework, ERSR, for fetal head ultrasound segmentation. Our framework consists of the dual-scoring adaptive filtering strategy, the ellipse-constrained pseudo-label refinement, and the symmetry-based multiple consistency regularization. The dual-scoring adaptive filtering strategy uses boundary consistency and contour regularity criteria to evaluate and filter teacher outputs. The ellipse-constrained pseudo-label refinement refines these filtered outputs by fitting least-squares ellipses, which strengthens pixels near the center of the fitted ellipse and suppresses noise simultaneously. The symmetry-based multiple consistency regularization enforces multi-level consistency across perturbed images, symmetric regions, and between original predictions and pseudo-labels, enabling the model to capture robust and stable shape representations. Our method achieves state-of-the-art performance on two benchmarks. On the HC18 dataset, it reaches Dice scores of 92.05% and 95.36% with 10% and 20% labeled data, respectively. On the PSFH dataset, the scores are 91.68% and 93.70% under the same settings.
Abstract (translated)
胎儿头部在超声图像中的自动分割对于产前监测至关重要，然而由于超声图像质量差以及标注数据不足的问题，实现稳健的分割仍然具有挑战性。半监督方法可以缓解缺乏标注数据的问题，但在处理胎儿头部超声图像的独特特性时却显得力不从心，因此生成可靠的伪标签和实施有效的一致性正则化约束颇具难度。为了解决这些问题，我们提出了一种新的半监督框架ERSR（Enhanced Robust Semi-supervised Regression），用于胎儿头部超声分割。该框架包括双评分自适应过滤策略、椭圆约束的伪标签精炼以及基于对称性的多级一致性正则化。 1. 双评分自适应过滤策略利用边界一致性和轮廓规则性标准来评估和筛选教师输出。 2. 椭圆约束的伪标签精炼通过拟合最小二乘椭圆来优化这些经过筛选的输出，该方法增强了拟合椭圆中心附近的像素，并同时抑制噪声。 3. 基于对称性的多级一致性正则化在扰动图像、对称区域以及原始预测与伪标签之间强制执行多层次的一致性，使模型能够捕获稳健且稳定的形状表示。 我们的方法在两个基准数据集上达到了最先进的性能。在HC18数据集中，分别使用10%和20%的标注数据时，Dice得分分别为92.05%和95.36%；而在PSFH数据集中，在相同设置下，Dice得分为91.68%和93.70%。
URL
https://arxiv.org/abs/2508.19815
