Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography.
Published
Journal Article
Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up the quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance. In this paper, we present the Cramèr-Rao lower bounds (CRLBs) for the problem of OCT layer segmentation. In deriving the CRLBs, we address the important problem of defining statistical models that best represent the intensity distribution in each layer of the retina. Additionally, we calculate the bounds under an optimal affine bias, reflecting the use of prior knowledge in many segmentation algorithms. Experiments using in vivo images of human retina from a commercial spectral domain OCT system are presented, showing potential for improvement of automated segmentation accuracy. Our general mathematical model can be easily adapted for virtually any OCT system. Furthermore, the statistical models of signal and noise developed in this paper can be utilized for the future improvements of OCT image denoising, reconstruction, and many other applications.
Full Text
Duke Authors
Cited Authors
- Dubose, TB; Cunefare, D; Cole, E; Milanfar, P; Izatt, JA; Farsiu, S
Published Date
- September 2018
Published In
Volume / Issue
- 37 / 9
Start / End Page
- 1978 - 1988
PubMed ID
- 29990154
Pubmed Central ID
- 29990154
Electronic International Standard Serial Number (EISSN)
- 1558-254X
International Standard Serial Number (ISSN)
- 0278-0062
Digital Object Identifier (DOI)
- 10.1109/tmi.2017.2772963
Language
- eng