ArticleIEEE transactions on medical imaging2018
Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Endoscopic optical coherence tomography angiography using inverse SNR-amplitude decorrelation features and electrothermal micro-electro-mechanical system raster scan.Quantitative imaging in medicine and surgery · 2022Article
- Statistical modeling of retinal optical coherence tomography using the Weibull mixture model.Biomedical optics express · 2021Article
- UnifiedAdvances in optics and photonics · 2021Article
- Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography.Biomedical optics express · 2020Article
- Approaches to quantify optical coherence tomography angiography metrics.Annals of translational medicine · 2020Review
- Automated Deformation-Based Analysis of 3D Optical Coherence Tomography in Diabetic Retinopathy.IEEE transactions on medical imaging · 2020Article
- Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?Biomedical optics express · 2019Article
- Temporal speckle-averaging of optical coherence tomography volumes forNeurophotonics · 2019Article
- Parallel deep neural networks for endoscopic OCT image segmentation.Biomedical optics express · 2019Article
- Aperture phase modulation with adaptive optics: a novel approach for speckle reduction and structure extraction in optical coherence tomography.Biomedical optics express · 2019Article
- Automatic three-dimensional segmentation of endoscopic airway OCT images.Biomedical optics express · 2019Article
- Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT.Biomedical optics express · 2018Article
- Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2.Biomedical optics express · 2018Article
- Comparison of chorioretinal layers in rhesus macaques using spectral-domain optical coherence tomography and high-resolution histological sections.Experimental eye research · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
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.
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