Improving Semantic Segmentation of Microscopy Images Using Rotation Equivariant Convolutional Networks
Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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.
Description
Keywords
Computer science, biomedicine, machine learning, convolutional neural networks, segmentation, image analysis