Abstract: Breast ultrasound image segmentation plays a significant role in computer-aided diagnosis, but existing methods are constrained by the bottleneck of scarce annotated data. In recent years, generative models have demonstrated potential in medical image synthesis, yet current approaches struggle to simultaneously ensure image realism and mask semantic consistency. To address the performance bottleneck of segmentation models caused by the limited scale of ultrasound image datasets, this paper proposes an innovative ultrasound image dataset augmentation method. First, from a pathological perspective, we design a mask generation module based on the characteristics of benign and malignant tumors, which efficiently generates multiple semantically plausible masks. Next, to synthesize ultrasound images corresponding to these masks, we propose a Mask-guided Diffusion Model (MDM). This model incorporates mask information into the denoising network of the diffusion model through normalization methods, thereby generating ultrasound images that exhibit high semantic consistency with the masks. Experimental results demonstrate that the proposed method significantly outperforms mainstream generative models in terms of image fidelity (FID) and semantic alignment (mIoU). By validating the strategy of incrementally generating data, the performance of segmentation models improves markedly with increasing data volume, proving the effectiveness of the synthesized data.

摘要： 乳腺超声图像分割在计算机辅助诊断中具有重要意义，而现有方法受限于标注数据稀缺的瓶颈。近年来，生成模型在医学图像合成领域展现出潜力，但现有方法难以同时保证图像真实性与掩码语义一致性。针对超声图像数据集规模较小所带来的分割模型性能瓶颈，本文提出了一种创新的超声图像数据集扩增方法。首先，结合病理学视角，根据良性肿瘤与恶性肿瘤的特征，设计了一个掩码生成模块，该模块能够高效生成多个语义合理的掩码。接着，为了生成与这些掩码相对应的超声图像，本文提出了一种掩码引导的扩散模型（MDM）。该模型通过归一化方法将掩码信息引入扩散模型的去噪网络，从而生成语义上与掩码高度一致的超声图像。实验表明，所提方法在图像保真度（FID）和语义对齐（mIoU）指标上均取得显著优于主流生成模型。通过逐步增量生成数据的策略验证，分割模型的性能随数据量增加显著提升，证明了生成数据的有效性。