Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
This addresses the need for more interpretable and anatomically accurate image generation in medical imaging, though it is incremental as it builds on existing diffusion models with a novel deformation-based approach.
The paper tackled the problem of diffusion models in medical imaging lacking interpretable connections and creating anatomically implausible structures by proposing the Deformation-Recovery Diffusion Model (DRDM), which uses deformation fields for morphological transformation, resulting in diverse and anatomically plausible deformations that improved performance in tasks like segmentation and registration.
In medical imaging, the diffusion models have shown great potential for synthetic image generation tasks. However, these approaches often lack the interpretable connections between the generated and real images and can create anatomically implausible structures or illusions. To address these limitations, we propose the Deformation-Recovery Diffusion Model (DRDM), a novel diffusion-based generative model that emphasises morphological transformation through deformation fields rather than direct image synthesis. DRDM introduces a topology-preserving deformation field generation strategy, which randomly samples and integrates multi-scale Deformation Velocity Fields (DVFs). DRDM is trained to learn to recover unrealistic deformation components, thus restoring randomly deformed images to a realistic distribution. This formulation enables the generation of diverse yet anatomically plausible deformations that preserve structural integrity, thereby improving data augmentation and synthesis for downstream tasks such as few-shot learning and image registration. Experiments on cardiac Magnetic Resonance Imaging and pulmonary Computed Tomography show that DRDM is capable of creating diverse, large-scale deformations, while maintaining anatomical plausibility of deformation fields. Additional evaluations on 2D image segmentation and 3D image registration tasks indicate notable performance gains, underscoring DRDM's potential to enhance both image manipulation and generative modelling in medical imaging applications. Project page: https://jianqingzheng.github.io/def_diff_rec/