Segmentation masks of pathological areas are useful in many medical applications, such as brain tumour and stroke management. Moreover, healthy counterfactuals of diseased images can be used to enhance radiologists' training files and to improve the interpretability of segmentation models. In this work, we present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. To do so, we start by considering a saliency map that approximately covers the pathological areas, obtained with ACAT. Then, we propose a technique that allows to perform targeted modifications to these regions, while preserving the rest of the image. In particular, we employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Diffusion Implicit Model (DDIM) at each step of the sampling process. DDPM is used to modify the areas affected by a lesion within the saliency map, while DDIM guarantees reconstruction of the normal anatomy outside of it. The two parts are also fused at each timestep, to guarantee the generation of a sample with a coherent appearance and a seamless transition between edited and unedited parts. We verify that when our method is applied to healthy samples, the input images are reconstructed without significant modifications. We compare our approach with alternative weakly supervised methods on IST-3 for stroke lesion segmentation and on BraTS2021 for brain tumour segmentation, where we improve the DICE score of the best competing method from $0.6534$ to $0.7056$.

翻译：病理区域的分割掩膜在众多医学应用中具有重要价值，例如脑肿瘤与脑卒中的临床管理。此外，病变图像的健康反事实可用于丰富放射科医师培训资料，并提升分割模型的可解释性。本研究提出一种弱监督方法，首先生成病变图像的健康版本，进而获取像素级异常图谱。具体而言，我们从基于ACAT方法获取的近似覆盖病理区域的显著性图出发，提出一种能对上述区域进行靶向修改同时保留图像其余部分的技术。我们采用在健康样本上训练的扩散模型，在采样过程的每个步骤中结合去噪扩散概率模型（DDPM）与去噪扩散隐式模型（DDIM）。DDPM用于修改显著性图内受病灶影响的区域，而DDIM则确保对正常解剖结构的重建。两个模块在每个时间步进行融合，以生成外观连贯的样本，并实现编辑区域与未编辑区域的无缝过渡。实验验证表明，当该方法应用于健康样本时，输入图像在无显著修改的情况下得以重建。我们针对IST-3数据集上的卒中病灶分割任务与BraTS2021数据集上的脑肿瘤分割任务，将本方法与替代弱监督方法进行对比，使最优竞争方法的DICE得分从0.6534提升至0.7056。