Knowledge-Guided Multi-Task Diffusion with Type-Aware Boundary Modeling for Lesion Synthesis

Annotated pathological CT data remain scarce, limiting the generalization of lesion segmentation models. Diffusion-based synthesis offers scalable data augmentation, but mask- or prompt-conditioned generation alone does not enforce measurable radiological properties or realistic lesion–tissue transitions; joint generation and segmentation can also produce conflicting gradients. We propose a knowledge-guided lesion synthesis framework with two complementary components. Knowledge-guided multi-task lesion synthesis (KG-MTS) incorporates CT priors as differentiable constraints on lesion density, tissue composition, and internal heterogeneity, and introduces auxiliary segmentation through asymmetric conflict-aware weighting that attenuates harmful segmentation updates while preserving the generation direction. Type-aware boundary transition modeling (TA-BTM) represents the lesion boundary as a phenotype-dependent intensity transition zone and aligns its signed contrast, effective width, and smoothness with real-lesion statistics. Given a CT region of interest, a prescribed mask, and a structured radiological prompt, the model synthesizes controllable lesions while preserving the surrounding anatomy. We evaluate pulmonary nodule and renal tumor synthesis on LIDC-IDRI and KiTS23 using three downstream segmentation architectures. Compared with the reproduced LeFusion baseline, our method achieves Dice and NSD improvements of 2.80–3.20% and 2.04–2.38% on LIDC-IDRI, and 1.73–3.05% and 1.31–1.99% on KiTS23, respectively. Ablations further show that KG-MTS and TA-BTM provide complementary improvements in volumetric overlap and boundary agreement. Code: https://anonymous.4open.science/r/lesion-synthesis-review-1C7D