论文标题
学习组织组织的生成模型
Learning Generative Models of Tissue Organization with Supervised GANs
论文作者
论文摘要
理解细胞和组织空间组织的关键步骤是构建准确反映该组织的生成模型的能力。在本文中，我们专注于构建电子显微镜（EM）图像的生成模型，其中细胞膜和线粒体的位置已被密集注释，并提出了一个两阶段的程序，该程序以监督的方式使用生成的对抗网络（或GAN）产生逼真的图像。在第一阶段，我们将标签“图像”综合为噪声“图像”作为输入，然后在第二阶段提供了EM图像合成的监督。完整的模型自然会生成标签图像对。我们表明，使用（1）形状特征和全局统计数据，（2）分割精确度和（3）用户研究来生产准确的合成EM图像。我们还通过在中间合成标签上实施重建损失，从而将两个阶段统一为一个端到端框架，从而证明了进一步的改进。
A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization. In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated, and propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way. In the first stage, we synthesize a label "image" given a noise "image" as input, which then provides supervision for EM image synthesis in the second stage. The full model naturally generates label-image pairs. We show that accurate synthetic EM images are produced using assessment via (1) shape features and global statistics, (2) segmentation accuracies, and (3) user studies. We also demonstrate further improvements by enforcing a reconstruction loss on intermediate synthetic labels and thus unifying the two stages into one single end-to-end framework.
