Table2Cell: generating realistic nuclei images from numeric properties for data compression

Abstract

Microscopy image analysis is the process of extracting quantitative information from images obtained from microscopes. It involves techniques and methods from computer vision, image processing and machine learning to identify and extract numerical features from images of biological samples such as cells. Modern software is capable of extracting a great number of these properties from images of cells. Provided that there are hundreds of cells per one image and thousands of images per experiment, the amount of data extracted becomes a significant computational burden. In this work, we address the problem of feature selection using image generation with neural networks. Here we show that by generating cell images from different sets of numeric characteristics and assessing the resulting image quality we can decide which input parameters are essential and which can be discarded, helping us to perform feature selection. We propose a novel Table2Cell model that can generate high-quality nuclei images from vectors of features. Our results demonstrate that the generated images have a high degree of similarity to real images, and that the Table2Cell model is responsive to variations in its input parameters. This study not only addresses the issue of feature selection, but also has broader implications for the field of image generation. We believe that the results of our research provide valuable insights for further research and development of this technology.