School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
Med Image Anal. 2023 Feb;84:102708. doi: 10.1016/j.media.2022.102708. Epub 2022 Dec 5.
Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR images. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including the shape, the size, and the texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation strategy on greatly improving nodule detection performance.
在胸部 X 射线（CXR）图像中检测肺结节是早期肺癌筛查的常见方法。基于深度学习的计算机辅助诊断（CAD）系统可以为 CXR 图像中的结节筛查提供支持。然而，这需要大规模的、多样化的高质量标注医疗数据来训练这种强大和准确的 CAD。为了缓解此类数据集的有限可用性，提出了肺结节合成方法来进行数据扩充。然而，以前的方法缺乏生成与探测器所需的形状/大小属性相匹配的真实结节的能力。为了解决这个问题，我们在本文中引入了一种新的肺结节合成框架，该框架将结节属性分解为形状、大小和纹理三个主要方面，分别进行建模。基于 GAN 的形状生成器首先通过生成多样化的形状掩模来建模结节形状。随后的大小调制允许以像素级的粒度对生成的结节形状的直径进行定量控制。最后，一个粗到细的门控卷积纹理生成器根据调制的形状掩模合成具有视觉可信度的结节纹理。此外，我们提出通过控制解耦的结节属性来合成结节 CXR 图像，以便更好地补偿检测任务中容易错过的结节。我们的实验证明了所提出的肺结节合成框架在图像质量、多样性和可控性方面的增强效果。我们还验证了我们的数据扩充策略在大大提高结节检测性能方面的有效性。
