The segmentation of mass lesions in digital breast tomosynthesis (DBT) images is very significant for the early screening of breast cancer. However, the high-density breast tissue often leads to high concealment of the mass lesions, which makes manual annotation difficult and time-consuming. As a result, there is a lack of annotated data for model training. Diffusion models are commonly used for data augmentation, but the existing methods face two challenges. First, due to the high concealment of lesions, it is difficult for the model to learn the features of the lesion area. This leads to the low generation quality of the lesion areas, thus limiting the quality of the generated images. Second, existing methods can only generate images and cannot generate corresponding annotations, which restricts the usability of the generated images in supervised training. In this work, we propose a paired image generation method. The method does not require external conditions and can achieve the generation of paired images by training an extra diffusion guider for the conditional diffusion model. During the experimental phase, we generated paired DBT slices and mass lesion masks. Then, we incorporated them into the supervised training process of the mass lesion segmentation task. The experimental results show that our method can improve the generation quality without external conditions. Moreover, it contributes to alleviating the shortage of annotated data, thus enhancing the performance of downstream tasks. The source code is available at https://github.com/zhanghx1320/PIG.

翻译：数字乳腺断层合成图像中肿块病灶的分割对于乳腺癌的早期筛查具有重要意义。然而，高密度乳腺组织常导致肿块病灶隐蔽性高，使得人工标注困难且耗时。因此，模型训练缺乏标注数据。扩散模型通常用于数据增强，但现有方法面临两个挑战。首先，由于病灶隐蔽性高，模型难以学习病灶区域的特征。这导致病灶区域的生成质量较低，从而限制了生成图像的质量。其次，现有方法只能生成图像，无法生成相应的标注，这限制了生成图像在监督训练中的可用性。在本工作中，我们提出了一种配对图像生成方法。该方法无需外部条件，通过为条件扩散模型训练一个额外的扩散引导器，即可实现配对图像的生成。在实验阶段，我们生成了配对的DBT切片与肿块病灶掩码。随后，我们将它们纳入肿块病灶分割任务的监督训练过程中。实验结果表明，我们的方法无需外部条件即可提高生成质量。此外，它有助于缓解标注数据短缺的问题，从而提升下游任务的性能。源代码可在 https://github.com/zhanghx1320/PIG 获取。