Deep Bayesian approach to eye fundus image segmentation
Garifullin, Azat (2021-12-09)
Väitöskirja
09.12.2021
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Engineering Science
School of Engineering Science, Laskennallinen tekniikka
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-762-4
https://urn.fi/URN:ISBN:978-952-335-762-4
Tiivistelmä
Eye diseases cause different retinal abnormalities that can be detected and diagnosed by examining eye fundus images. Due to the rapidly growing amount of data, there is a need for methods that are able to produce meaningful image representations and analysis results helping medical doctors to make correct diagnoses. Recent advances in deep learning have enabled very promising approaches for solving a variety of tasks related to automatic fundus image analysis. However, there is growing concern about the reliability of these methods and possible issues exist regarding their utilization in risk-sensitive scenarios.
This study extends the current research by studying fundus image segmentation from a deep Bayesian perspective that permits model parameters and their outputs to be treated as random variables. The treatment makes it possible to estimate how uncertain the model is about its predictions. The study focuses on subproblems including the segmentation of the retinal vasculature, optic disc, macula and diabetic retinopathy lesions. Considering the probabilistic nature of the chosen methods, validation procedures need to be augmented in order to evaluate not only the segmentation results but also the estimated uncertainties.
The experimental results show that the proposed Bayesian baselines for fundus image segmentation yield a performance that is comparable to the existing state-of-the-art approaches. The produced uncertainty estimates provide meaningful information about possible problems during the inference. However, the uncertainty validation results suggest that predicting misclassifications using uncertainty in a straightforward manner is limited. The results of additional experiments using weight averaging techniques and spectral image data are provided. This work also discusses the problems encountered when applying Bayesian methods to fundus image segmentation.
This study extends the current research by studying fundus image segmentation from a deep Bayesian perspective that permits model parameters and their outputs to be treated as random variables. The treatment makes it possible to estimate how uncertain the model is about its predictions. The study focuses on subproblems including the segmentation of the retinal vasculature, optic disc, macula and diabetic retinopathy lesions. Considering the probabilistic nature of the chosen methods, validation procedures need to be augmented in order to evaluate not only the segmentation results but also the estimated uncertainties.
The experimental results show that the proposed Bayesian baselines for fundus image segmentation yield a performance that is comparable to the existing state-of-the-art approaches. The produced uncertainty estimates provide meaningful information about possible problems during the inference. However, the uncertainty validation results suggest that predicting misclassifications using uncertainty in a straightforward manner is limited. The results of additional experiments using weight averaging techniques and spectral image data are provided. This work also discusses the problems encountered when applying Bayesian methods to fundus image segmentation.
Kokoelmat
- Väitöskirjat [1099]