论文标题
儿科计算机断层扫描与辅助分类器生成对抗网络的年龄条件合成
Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks
论文作者
论文摘要
深度学习是计算机断层扫描（CT）图像处理（例如器官分割）中流行而有力的工具，但其对大型培训数据集的要求仍然是一个挑战。即使儿童在成长过程中存在很大的解剖学变异性，但由于对儿童的辐射风险，小儿CT扫描的训练数据集尤其难以获得。在本文中，我们提出了一种使用新的辅助分类器生成的对抗网络（ACGAN）架构来有条件地合成现实的小儿CT图像的方法，并考虑到年龄信息。提出的网络生成了年龄条件的高分辨率CT图像，以丰富小儿培训数据集。
Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into account. The proposed network generated age-conditioned high-resolution CT images to enrich pediatric training datasets.
