{"ID":2881898,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11375","arxiv_id":"2508.11375","title":"AnatoMaskGAN: GNN-Driven Slice Feature Fusion and Noise Augmentation for Medical Semantic Image Synthesis","abstract":"Medical semantic-mask synthesis boosts data augmentation and analysis, yet most GAN-based approaches still produce one-to-one images and lack spatial consistency in complex scans. To address this, we propose AnatoMaskGAN, a novel synthesis framework that embeds slice-related spatial features to precisely aggregate inter-slice contextual dependencies, introduces diverse image-augmentation strategies, and optimizes deep feature learning to improve performance on complex medical images. Specifically, we design a GNN-based strongly correlated slice-feature fusion module to model spatial relationships between slices and integrate contextual information from neighboring slices, thereby capturing anatomical details more comprehensively; we introduce a three-dimensional spatial noise-injection strategy that weights and fuses spatial features with noise to enhance modeling of structural diversity; and we incorporate a grayscale-texture classifier to optimize grayscale distribution and texture representation during generation. Extensive experiments on the public L2R-OASIS and L2R-Abdomen CT datasets show that AnatoMaskGAN raises PSNR on L2R-OASIS to 26.50 dB (0.43 dB higher than the current state of the art) and achieves an SSIM of 0.8602 on L2R-Abdomen CT--a 0.48 percentage-point gain over the best model, demonstrating its superiority in reconstruction accuracy and perceptual quality. Ablation studies that successively remove the slice-feature fusion module, spatial 3D noise-injection strategy, and grayscale-texture classifier reveal that each component contributes significantly to PSNR, SSIM, and LPIPS, further confirming the independent value of each core design in enhancing reconstruction accuracy and perceptual quality.","short_abstract":"Medical semantic-mask synthesis boosts data augmentation and analysis, yet most GAN-based approaches still produce one-to-one images and lack spatial consistency in complex scans. To address this, we propose AnatoMaskGAN, a novel synthesis framework that embeds slice-related spatial features to precisely aggregate inte...","url_abs":"https://arxiv.org/abs/2508.11375","url_pdf":"https://arxiv.org/pdf/2508.11375v1","authors":"[\"Zonglin Wu\",\"Yule Xue\",\"Qianxiang Hu\",\"Yaoyao Feng\",\"Yuqi Ma\",\"Shanxiong Chen\"]","published":"2025-08-15T10:19:38Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\",\"Graph Neural Network\"]","has_code":false}