CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion

• URL: http://arxiv.org/abs/2503.06919v1
• Date: Mon, 10 Mar 2025 04:59:54 GMT
• Title: CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion
• Authors: Weidong Guo, Hantao Zhang, Shouhong Wan, Bingbing Zou, Wanqin Wang, Chenyang Qiu, Peiquan Jin,
• Abstract summary: We introduce CAFusion, a novel approach for synthesizing perirectal lymph nodes.<n>By leveraging Signed Distance Functions (SDF), CAFusion generates highly realistic 3D anatomical structures.<n> Experimental results demonstrate that our synthetic data substantially improve segmentation performance.
• Score: 8.311453061101899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lesion synthesis methods have made significant progress in generating large-scale synthetic datasets. However, existing approaches predominantly focus on texture synthesis and often fail to accurately model masks for anatomically complex lesions. Additionally, these methods typically lack precise control over the synthesis process. For example, perirectal lymph nodes, which range in diameter from 1 mm to 10 mm, exhibit irregular and intricate contours that are challenging for current techniques to replicate faithfully. To address these limitations, we introduce CAFusion, a novel approach for synthesizing perirectal lymph nodes. By leveraging Signed Distance Functions (SDF), CAFusion generates highly realistic 3D anatomical structures. Furthermore, it offers flexible control over both anatomical and textural features by decoupling the generation of morphological attributes (such as shape, size, and position) from textural characteristics, including signal intensity. Experimental results demonstrate that our synthetic data substantially improve segmentation performance, achieving a 6.45% increase in the Dice coefficient. In the visual Turing test, experienced radiologists found it challenging to distinguish between synthetic and real lesions, highlighting the high degree of realism and anatomical accuracy achieved by our approach. These findings validate the effectiveness of our method in generating high-quality synthetic lesions for advancing medical image processing applications.

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