The insufficiency of annotated medical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new richer generative adversarial network for free-form 3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated–gated generator (RicherDG) and a hybrid loss function. The RicherDG has dilated–gated convolution layers to enable tumor-painting and to enlarge perceptive fields; and it has a novel richer convolutional feature association branch to recover multi-scale convolutional features especially from uncertain boundaries between tumor and surrounding healthy tissues. The hybrid loss function, which consists of a diverse range of losses, is designed to aggregate complementary information to improve optimization. We perform a comprehensive evaluation of the synthesis results on a wide range of public CT image datasets covering the liver, kidney tumors, and lung nodules. The qualitative and quantitative evaluations and ablation study demonstrated improved synthesizing results over advanced tumor synthesis methods.

带有注释的医学影像扫描不足以治疗癌症，因此难以在精密肿瘤学中训练和验证需要大量数据的深度学习模型。我们为计算机断层扫描（CT）图像中的自由形式3D肿瘤/病变合成提出了一种新的更丰富的对抗网络。该网络由新的更丰富的卷积特征增强的门控门限发生器（RicherDG）和混合损失函数组成。RicherDG具有扩张门控的卷积层，可以绘制肿瘤并扩大感知范围。并且它具有新颖的更丰富的卷积特征关联分支，可以恢复多尺度的卷积特征，尤其是从肿瘤与周围健康组织之间的不确定边界中恢复。混合损失函数由多种损失组成，用于汇总补充信息以改善优化。我们对涵盖肝脏，肾脏肿瘤和肺结节的各种公共CT图像数据集进行综合结果的综合评估。定性和定量评估以及消融研究表明，合成结果优于先进的肿瘤合成方法。