Plasticine: A Traceable Diffusion Model for Medical Image Translation

TL;DR

Plasticine is a novel diffusion-based image translation framework for medical images that ensures pixel-level traceability, improving clinical interpretability by maintaining spatial correspondence during translation.

Contribution

It introduces the first end-to-end diffusion model for medical image translation that explicitly incorporates traceability as a core feature.

Findings

Supports pixel-wise traceability in medical image translation

Generates spatially coherent deformations and intensity transitions

Enhances clinical interpretability of translated images

Abstract

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.