Latent Diffusion With Topology-Preserving Conditioning for Structure-Aware Data Generation

Conditional latent diffusion has become a promising paradigm for synthesizing paired image-mask data for segmentation, yet preserving structural topology remains challenging for curvilinear objects such as vessels and cracks. Existing methods mainly focus on how to inject spatial conditions into generative backbones, while paying limited attention to whether the low-resolution condition representations themselves remain topologically reliable. As a result, mask-conditioned diffusion often suffers from two coupled failure modes: topology-breaking low-resolution projection of thin structural masks and progressive attenuation of structural cues during denoising. To address these issues, we propose TopoSegDiff, a topology-preserving conditioning framework for latent diffusion built on an adaptive teacher-student-adapter pipeline. Specifically, an adaptive topology constructor (ATC) first generates multiscale topology-preserving teacher conditions from the input mask. A lightweight topology distillation encoder (TDE) then amortizes these teacher conditions into feed-forward soft topology cues together with scale-wise reliability estimates. Finally, a reliability-aware topology adapter (RTA) injects the predicted conditions into the diffusion decoder, while a reliability-aware topology alignment objective regularizes intermediate features against topology-preserving targets. In this way, topology-preserving supervision is transformed into scalable and differentiable guidance for structure-aware generation. Experiments on six public curvilinear-segmentation benchmarks across three application domains show that TopoSegDiff produces more realistic and structurally faithful image-mask pairs, reduces topology inconsistency, and consistently improves downstream segmentation performance when used for synthetic augmentation. These results support topology-preserving conditioning as an effective route toward more reliable structure-aware generation and learning for curvilinear objects.