In this paper, two methods for expanding a set of images were considered and it was examined how their implementation affects one example of a neural network that performs semantic segmentation of cross-sectional images of the heart. The first method involves the use of basic transformations in images such as: mirroring, rotation and translation. The second method uses the implementation of a neural network of a generative adversarial model (GAN) that generates new images. GAN is a neural network model that consists of two neural networks that "compete" against each other. The first one, the generator, tries to generate images as similar as possible to those in the original data set, and the second one, the discriminator, tries to distinguish the images created by the generator from those in the initial data set. The paper also mentions metrics for evaluating neural networks that perform pattern recognition in images; IoU and Dice. Both are derived as equations of areas that are well classified by the network and areas that are incorrectly classified by the network. Various technologies were used to implement the methods, such as Pytorch, a library with the functions needed to build neural networks. GAN was implemented by changing various hyper-parameters and layers until the desired result was achieved. Finally, the methods were implemented on one example of a neural network. According to the results, it was concluded that in this example the independent implementation of methods is not enough and that it is shown that independent implementation of methods without changing hyper-parameters and layers in the network architecture can negatively affect its operation.