Structure preserving adversarial generation of labeled training samples for single cell segmentation

Supplementary package (dataset, generated data and code) for the article: "Structure preserving adversarial generation of labeled training samples for single cell segmentation". (Cell Reports Methods, 2023 - under submission) Content of the package: datasets.zip: the images and annotations for the fallopian tube and salivary gland dataset. mask_quality_experiment.zip: the generated masks using SIMCEP and the generated microscopy images using pix2pix using the SIMCEP masks. pix2pix_models.zip: the models for fold 0 for both datasets. It can be used to synthesize new images. StyleGAN2-ada_models.zip: the models for synthesizing new masks. code.zip: a github repository (named .git) for executing the pipeline.本数据包为论文《面向单细胞分割的保留结构对抗生成标注训练样本》（Structure preserving adversarial generation of labeled training samples for single cell segmentation）的配套资源，涵盖数据集、生成数据与代码，该论文已投稿至《Cell Reports Methods》，2023年（投稿中）。数据包内容如下：datasets.zip：收录输卵管与唾液腺数据集的图像及标注文件；mask_quality_experiment.zip：包含采用SIMCEP生成的掩码，以及基于该掩码通过pix2pix生成的显微图像；pix2pix_models.zip：包含两个数据集各对应第0折的模型，可用于合成新图像；StyleGAN2-ada_models.zip：包含用于生成新掩码的模型；code.zip：包含一个命名为.git的GitHub仓库，用于执行整套实验流程。