The automatic segmentation of epithelial and stromal regions in breast pathological images has very important clinical significance for the diagnosis and treatment of breast cancer. However, due to the high complexity of epithelial and stromal regions in breast tissue pathological images, it is difficult for general segmentation models to effectively train the model based on the provided segmentation labels only, and perform fast and accurate segmentation of the two regions. Therefore, based on conditional Generative Adversarial Network (cGAN), the EPithelium and Stroma segmentation conditional Generative Adversarial Network (EPScGAN) model was proposed. In EPScGAN, the discrimination mechanism of the discriminator provided a trainable loss function for the training of the generator, in order to measure the error between the segmentation result outputs of the generator and the real labels more accurately, so as to better guide the generator training. Total of 1 286 images with the size of 512×512 were randomly cropped as an experimental dataset from the expert-labeled breast pathological image datasets provided by the Netherlands Cancer Institute (NKI) and the Vancouver General Hospital (VGH). Then the dataset was divided into the training set and the test set according to the ratio of 7:3 to train and test the EPScGAN model. Experimental results show that, the mean Intersection over Union (mIoU) of the EPScGAN model on the test set is 78.12%, and compared with other 6 popular deep learning segmentation models, EPScGAN model has better segmentation performance.