Anatomic-Constrained Medical Image Synthesis via Physiological Density Sampling

Despite substantial progress in utilizing deep learning methods for clinical diagnosis, their efficacy depends on sufficient annotated data, which is often limited available owing to the extensive manual efforts required for labeling. Although prevalent data synthesis techniques can mitigate such data scarcity, they risk generating outputs with distorted anatomy that poorly represent real-world data. We address this challenge through a novel integration of anatomically constrained synthesis with registration uncertainty-based refinement, termed Anatomic-Constrained medical Image Synthesis (ACIS). Specifically, we (1) generate the pseudo-mask via the physiological density estimation and Voronoi tessellation to represent the spatial anatomical information as the image synthesis prior; (2) synthesize diverse yet realistic image-annotation guided by the pseudo-masks, and (3) refine the outputs by registration uncertainty estimation to encourage the anatomical consistency between synthesized and real-world images. We validate ACIS for improving performance in both segmentation and image reconstruction tasks for few-shot learning. Experiments across diverse datasets demonstrate that ACIS outperforms state-of-the-art image synthesis techniques and enables models trained on only \(10\%\) or less of the total training data to achieve comparable or superior performance to that of models trained on complete datasets. The source code is publicly available at https://github.com/Arturia-Pendragon-Iris/VonoroiGeneration .