Transfer learning methods for glioma C6 cell segmentation

In this paper, we present an algorithm for binary segmentation of glioma C6 cells using deep learning methods to simplify and speed up the analysis of this culture growth. The first of its kind dataset containing 30 microscopic phase-contrast images of glioma C6 cells is collected to design and test the algorithm. We explore the influence of the encoder architecture in the neural network segmenter on the accuracy of glioma cell segmentation. Transfer learning approaches using the LIVECell dataset of microscopic images and the large ImageNet dataset of non-specialized images are used since the collected dataset contains a relatively small number of images. Experiments show that pre-training the neural network on LIVECell provides a significant advantage in low-resolution glioma cell recognition, with encoders trained on ImageNet providing better results at higher resolution. The paper proposes ways to improve the generalizing abilities of LIVECell weights to work at high resolution by applying augmentation. We demonstrate that using different starting weights allows us to obtain different generalization properties beyond the training set, which can be useful when detecting, or excluding from consideration, other cells in an image.