Coarse-to-Fine Medical Image Translation by Incorporating Deterministic Guidance and Probabilistic Refinement

Hongnian Tian, Tianxu Lv, Jiansong Fan, Delin Pan, Lihua Li, Xiang Pan

Abstract: In clinical diagnosis and treatment, traditional enhanced imaging techniques often suffer from inherent limitations such as high time costs and radiation risks. Therefore, medical image translation technology provides an efficient and cost-effective alternative. However, images generated by existing medical image generation methods still face challenges, such as a lack of structural consistency and blurred local details. Most methods struggle to simultaneously integrate deterministic structural information, such as anatomical priors, and probabilistic dynamic variations, such as blood flow changes, to guide image generation. To address these challenges, we propose a Coarse-to-Fine Medical Image Translation (C2FMIT) model, which incorporates Deterministic Guidance and Probabilistic Refinement to balance generation controllability and fidelity. First, we design a Deterministic Guidance Branch (DGB) to extract coarse-grained features, such as organ contours, to provide global structural constraints. Then, these deterministic priors are fused into our Probabilistic Refinement Branch (PRB), where the Brownian Bridge diffusion is employed for fine-grained optimization, enhancing microvascular textures and dynamic enhancement regions. Notably, we designed a Coarse-to-Fine Guided Attention Module (C2FGAM) to achieve progressive optimization from global structure to local details. Experimental results demonstrate that our method achieves superior performance across multiple modalities of functionally contrast-enhanced medical imaging on both public and in-house datasets.

External IDs:dblp:conf/miccai/TianLFPLP25