Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, USA.
Med Image Anal. 2021 Jul;71:102060. doi: 10.1016/j.media.2021.102060. Epub 2021 Apr 20.
The dearth of annotated data is a major hurdle in building reliable image segmentation models. Manual annotation of medical images is tedious, time-consuming, and significantly variable across imaging modalities. The need for annotation can be ameliorated by leveraging an annotation-rich source modality in learning a segmentation model for an annotation-poor target modality. In this paper, we introduce a diverse data augmentation generative adversarial network (DDA-GAN) to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. This is achieved by generating diverse augmented data for the target domain by one-to-many source-to-target translation. The DDA-GAN uses unpaired images from the source and target domains and is an end-to-end convolutional neural network that (i) explicitly disentangles domain-invariant structural features related to segmentation from domain-specific appearance features, (ii) combines structural features from the source domain with appearance features randomly sampled from the target domain for data augmentation, and (iii) train the segmentation model with the augmented data in the target domain and the annotations from the source domain. The effectiveness of our method is demonstrated both qualitatively and quantitatively in comparison with the state of the art for segmentation of craniomaxillofacial bony structures via MRI and cardiac substructures via CT.
注释数据的缺乏是构建可靠图像分割模型的主要障碍。医学图像的手动注释既繁琐又耗时，并且在不同的成像模式下变化很大。通过利用注释丰富的源模式在学习注释较少的目标模式的分割模型，可以减轻注释的需求。在本文中，我们引入了一种多样化的数据增强生成对抗网络（DDA-GAN），通过从注释的源图像域借用信息来训练未注释的目标图像域的分割模型。这是通过对目标域进行一对一到多对的源到目标翻译来生成多样化的增强数据来实现的。DDA-GAN 使用来自源域和目标域的未配对图像，是一个端到端卷积神经网络，它 (i) 显式分离与分割相关的域不变结构特征与域特定外观特征，(ii) 将来自源域的结构特征与从目标域随机采样的外观特征相结合进行数据增强，以及 (iii) 用目标域中的增强数据和源域中的注释来训练分割模型。通过与 MRI 分割颅颌面骨结构和 CT 分割心脏亚结构的最新技术进行定性和定量比较，证明了我们的方法的有效性。
