FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model

• URL: http://arxiv.org/abs/2503.09560v1
• Date: Wed, 12 Mar 2025 17:25:09 GMT
• Title: FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model
• Authors: Jiahao Xia, Yutao Hu, Yaolei Qi, Zhenliang Li, Wenqi Shao, Junjun He, Ying Fu, Longjiang Zhang, Guanyu Yang,
• Abstract summary: We propose the Fine-grained Cardiac image Synthesis framework, established on 3D template conditional diffusion model.<n>FCaS achieves precise cardiac structure generation using template-guided Conditional Diffusion Model (TCDM)<n>To alleviate the confusion caused by imprecise synthetic images, we propose a Confidence-aware Adaptive Learning (CAL) strategy.
• Score: 23.9686884119236
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Solving medical imaging data scarcity through semantic image generation has attracted significant attention in recent years. However, existing methods primarily focus on generating whole-organ or large-tissue structures, showing limited effectiveness for organs with fine-grained structure. Due to stringent topological consistency, fragile coronary features, and complex 3D morphological heterogeneity in cardiac imaging, accurately reconstructing fine-grained anatomical details of the heart remains a great challenge. To address this problem, in this paper, we propose the Fine-grained Cardiac image Synthesis(FCaS) framework, established on 3D template conditional diffusion model. FCaS achieves precise cardiac structure generation using Template-guided Conditional Diffusion Model (TCDM) through bidirectional mechanisms, which provides the fine-grained topological structure information of target image through the guidance of template. Meanwhile, we design a deformable Mask Generation Module (MGM) to mitigate the scarcity of high-quality and diverse reference mask in the generation process. Furthermore, to alleviate the confusion caused by imprecise synthetic images, we propose a Confidence-aware Adaptive Learning (CAL) strategy to facilitate the pre-training of downstream segmentation tasks. Specifically, we introduce the Skip-Sampling Variance (SSV) estimation to obtain confidence maps, which are subsequently employed to rectify the pre-training on downstream tasks. Experimental results demonstrate that images generated from FCaS achieves state-of-the-art performance in topological consistency and visual quality, which significantly facilitates the downstream tasks as well. Code will be released in the future.

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