Domain gaps arising from variations in imaging devices and population distributions pose significant challenges for machine learning in medical image analysis. Existing image-to-image translation methods primarily aim to learn mappings between domains, often generating diverse synthetic data with variations in anatomical scale and shape, but they usually overlook spatial correspondence during the translation process. For clinical applications, traceability, defined as the ability to provide pixel-level correspondences between original and translated images, is equally important. This property enhances clinical interpretability but has been largely overlooked in previous approaches. To address this gap, we propose Plasticine, which is, to the best of our knowledge, the first end-to-end image-to-image translation framework explicitly designed with traceability as a core objective. Our method combines intensity translation and spatial transformation within a denoising diffusion framework. This design enables the generation of synthetic images with interpretable intensity transitions and spatially coherent deformations, supporting pixel-wise traceability throughout the translation process.

翻译：成像设备与人群分布差异导致的领域差异给医学图像分析中的机器学习带来了重大挑战。现有的图像到图像翻译方法主要旨在学习领域间的映射关系，通常能生成具有解剖尺度与形状变化的多样化合成数据，但这些方法在翻译过程中往往忽略了空间对应关系。对于临床应用而言，可追溯性——即提供原始图像与翻译图像间像素级对应关系的能力——具有同等重要性。这一特性可增强临床可解释性，但在以往方法中大多被忽视。为弥补这一不足，我们提出了Plasticine模型。据我们所知，这是首个以可追溯性为核心目标而明确设计的端到端图像到图像翻译框架。我们的方法在去噪扩散框架中融合了强度转换与空间变换。该设计能够生成具有可解释强度过渡与空间连贯形变的合成图像，从而支持整个翻译过程中的像素级可追溯性。