Synthesis Image Editing for Attribute Evolution in the Pseudo-Temporal Sequence of Pulmonary Nodule Growth.

Abstract

Medical Mixed Reality (MR) has made significant progress in virtual surgery simulation and tumor teaching. This paper proposes a framework for pulmonary nodule attribute editing based on image feature consistency, achieving spatial alignment of multi-stage case data. To address the limitations of traditional time-image reconstruction, we design an adversarial siamese model architecture capable of synthesizing missing nodule images, completing temporal data, and fine-grained modeling of nodule growth. To tackle challenges such as deformation, background inconsistency, and attribute uncertainty in generated samples, we introduce a Denoising Diffusion Implicit Model (DDIM) and construct an attribute vector space for pathological feature editing. Additionally, we propose a separable image reconstruction strategy to enhance local feature stability. Extensive validation on the lung-specific LIDC-IDRI dataset demonstrates superior performance with SSIM of 97.5${\%}$ and LPIPS of 0.036. To further verify generalization capability, cross-organ testing on the liver-focused LiTS dataset achieves competitive results with SSIM of 85.0${\%}$ and LPIPS of 0.128. These outcomes provide strong technical support for high-fidelity virtual surgery and intelligent tumor teaching platforms.