Wang Cong, Gan Meng, Zhang Miao, Li Deyin
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
These authors contributed equally to this work and should be considered co-first authors.
Biomed Opt Express. 2020 May 18;11(6):3095-3110. doi: 10.1364/BOE.394715. eCollection 2020 Jun 1.
Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.
自动分割对于食管光学相干断层扫描（OCT）图像处理很重要，它能够为疾病诊断提供诸如形状和厚度等组织特征。由于训练集有限和层形状各异，现有的基于深度卷积网络的自动分割方法可能无法产生准确的分割结果。本研究提出了一种新颖的对抗卷积网络（ACN），使用通过对抗学习训练的卷积网络来分割食管OCT图像。所提出的框架包括一个生成器和一个判别器，两者都具有类似U-Net的全卷积架构。判别器是一个混合网络，它判别生成的结果是否真实，并同时实现像素分类。利用对抗训练，判别器变得更强大。此外，对抗损失能够编码像素的高阶关系，从而消除了后处理的需求。对豚鼠食管OCT图像进行分割的实验证实，ACN在像素分类准确性方面优于几个深度学习框架，并改善了分割结果。实验还展示了ACN在检测嗜酸性食管炎（EoE）（一种食管疾病）方面的潜在临床应用。
