TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Tumor data synthesis offers a promising solution to the shortage of annotated
medical datasets. However, current approaches either limit tumor diversity by
using predefined masks or employ computationally expensive two-stage processes
with multiple denoising steps, causing computational inefficiency.
Additionally, these methods typically rely on binary masks that fail to capture
the gradual transitions characteristic of tumor boundaries. We present
TumorGen, a novel Boundary-Aware Tumor-Mask Synthesis with Rectified Flow
Matching for efficient 3D tumor synthesis with three key components: a
Boundary-Aware Pseudo Mask Generation module that replaces strict binary masks
with flexible bounding boxes; a Spatial-Constraint Vector Field Estimator that
simultaneously synthesizes tumor latents and masks using rectified flow
matching to ensure computational efficiency; and a VAE-guided mask refiner that
enhances boundary realism. TumorGen significantly improves computational
efficiency by requiring fewer sampling steps while maintaining pathological
accuracy through coarse and fine-grained spatial constraints. Experimental
results demonstrate TumorGen's superior performance over existing tumor
synthesis methods in both efficiency and realism, offering a valuable
contribution to AI-driven cancer diagnostics.
