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.
Abstract (translated)
病灶合成方法在生成大规模合成数据集方面取得了显著进展。然而，现有方法主要集中在纹理合成上，并且通常无法准确建模解剖结构复杂的病灶掩码。此外，这些方法往往缺乏对合成过程的精确控制能力。例如，直肠旁淋巴结直径范围从1毫米到10毫米不等，其轮廓不规则且复杂，现有的技术很难忠实复制这种特征。为了解决这些问题，我们引入了一种名为CAFusion的新方法来合成直肠旁淋巴结。通过利用符号距离函数（SDF），CAFusion可以生成高度逼真的三维解剖结构。此外，它还提供了对解剖和纹理特性进行灵活控制的能力，将形态属性的生成（如形状、大小和位置）与包括信号强度在内的纹理特征相分离。实验结果显示，我们合成的数据显著提高了分割性能，在Dice系数上实现了6.45%的增长。在视觉图灵测试中，经验丰富的放射科医生难以区分合成病灶和真实病灶，这突显了我们的方法所达到的现实性和解剖准确性水平。这些发现验证了我们在生成高质量合成病变方面的方法的有效性，以推进医学图像处理应用的发展。
URL
https://arxiv.org/abs/2503.06919
