Improving Semantic Segmentation of Microscopy Images Using Rotation Equivariant Convolutional Networks

Abstract

The segmentation of the cell nuclei is one of the first steps in medical image analysis workflow. Organisations conducting experiments with image analysis are mainly pharmaceutical companies and biomedicine laboratories, which need to process vast amounts of data and quantify it. The goal of these experiments could be to produce new drugs or diagnose diseases. Due to advancements in deep learning, these processes of nuclei segmentation have been automated, and the level of accuracy is relatively high. However, new methods for improving the accuracy of the models are constantly proposed. One of these proposals uses rotation equivariant convolutional neural networks based on group theory. These networks can produce invariant predictions regardless of the rotation of the input object. This bachelor’s thesis shows that rotation equivariant convolutional neural networks improve the semantic segmentation of nuclei and increase the generalisation capabilities of a model trained on fluorescent images. Additionally, the work gives an overview of failed attempts with brightfield images, outlines the already existing rotation equivariant models on the internet and describes their implementation complexity.