Ultrasound (US) imaging is widely used in routine clinical practice due to its advantages of being radiation-free, cost-effective, and portable. However, the low reproducibility and quality of US images, combined with the scarcity of expert-level annotation, make the training of fully supervised segmentation models challenging. To address these issues, we propose a novel unsupervised anomaly detection framework based on a diffusion model that incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process. Synomaly noise introduces synthetic anomalies into healthy images during training, allowing the model to effectively learn anomaly removal. The multi-stage diffusion process is introduced to progressively denoise images, preserving fine details while improving the quality of anomaly-free reconstructions. The generated high-fidelity counterfactual healthy images can further enhance the interpretability of the segmentation models, as well as provide a reliable baseline for evaluating the extent of anomalies and supporting clinical decision-making. Notably, the unsupervised anomaly detection model is trained purely on healthy images, eliminating the need for anomalous training samples and pixel-level annotations. We validate the proposed approach on carotid US, brain MRI, and liver CT datasets. The experimental results demonstrate that the proposed framework outperforms existing state-of-the-art unsupervised anomaly detection methods, achieving performance comparable to fully supervised segmentation models in the US dataset. Additionally, ablation studies underline the importance of hyperparameter selection for Synomaly noise and the effectiveness of the multi-stage diffusion process in enhancing model performance.

翻译：超声（US）成像因其无辐射、成本效益高及便携等优势，在常规临床实践中被广泛应用。然而，超声图像的低可重复性与质量欠佳，加之专家级标注稀缺，使得全监督分割模型的训练面临挑战。为解决这些问题，我们提出了一种基于扩散模型的新型无监督异常检测框架，该框架融合了合成异常（Synomaly）噪声函数与多阶段扩散过程。Synomaly噪声在训练过程中将合成异常引入健康图像，使模型能有效学习异常去除。引入多阶段扩散过程旨在逐步对图像去噪，在提升无异常重建质量的同时保留细节特征。所生成的高保真反事实健康图像不仅能进一步增强分割模型的可解释性，还可为评估异常程度及支持临床决策提供可靠基线。值得注意的是，该无监督异常检测模型仅使用健康图像进行训练，无需异常训练样本及像素级标注。我们在颈动脉超声、脑部MRI及肝脏CT数据集上验证了所提方法。实验结果表明，该框架优于现有的先进无监督异常检测方法，在超声数据集上达到了与全监督分割模型相当的性能。此外，消融实验强调了Synomaly噪声超参数选择的重要性，以及多阶段扩散过程在提升模型性能方面的有效性。