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 the task of brain lesion segmentation, achieving the highest mean Dice and IoU scores among the models considered.

翻译：病理区域的分割掩码在脑肿瘤与中风管理等诸多医疗应用中具有重要价值。此外，患病图像的健康反事实样本可用于扩充放射科医师的训练资料，并提升分割模型的可解释性。本研究提出一种弱监督方法，首先生成患病图像的健康版本，进而据此获得像素级异常图谱。具体实现中，我们首先通过ACAT算法获取大致覆盖病理区域的显著性图谱，随后提出一种能够针对这些区域进行定向修改并保持图像其余部分完整的技术。该方法的核心在于利用健康样本训练的扩散模型，并在采样过程的每一步中融合去噪扩散概率模型（DDPM）与去噪扩散隐式模型（DDIM）：DDPM负责修改显著性图谱内的病灶影响区域，而DDIM确保图谱外正常解剖结构的重建。两部分在每步采样时进行融合，以保证生成样本具有协调的外观特征及编辑区域与未编辑区域间的自然过渡。实验验证表明，当本方法应用于健康样本时，输入图像能够被重建且未产生显著畸变。在脑部病灶分割任务中，本方法与现有弱监督方法进行对比，取得了最高的平均Dice系数与交并比评分。