A review of image processing and analysis of computed tomography images using deep learning methods.
Authors
Affiliations (5)
Affiliations (5)
- School of Chemistry and Physics, Queensland University of Technology (QUT), Brisbane, QLD, Australia. [email protected].
- Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, QLD, Australia. [email protected].
- School of Chemistry and Physics, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
- Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.
- Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
Abstract
The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of these networks is the capability to extract useful patterns from vast quantities of complex data, including images. As many branches of healthcare revolves around the generation, processing, and analysis of images, these techniques have become increasingly commonplace. This is especially true for radiotherapy, which relies on the use of anatomical and functional images from a range of imaging modalities, such as Computed Tomography (CT). The aim of this review is to provide an understanding of deep learning methodologies, including neural network types and structure, as well as linking these general concepts to medical CT image processing for radiotherapy. Specifically, it focusses on the stages of enhancement and analysis, incorporating image denoising, super-resolution, generation, registration, and segmentation, supported by examples of recent literature.