IEEE Trans Med Imaging. 2018 Sep;37(9):1978-1988. doi: 10.1109/TMI.2017.2772963. Epub 2017 Nov 13.
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.
光学相干断层扫描 (OCT) 通过对视网膜层的可视化和测量，彻底改变了眼科疾病的诊断和预后。为了加快疾病生物标志物的定量分析，越来越多的自动分割算法被提出，以估计视网膜层的边界位置。虽然这些算法近年来的性能有了显著提高，但有一个关键问题需要问，那就是我们距离 OCT 分割性能的理论极限还有多远。在本文中，我们提出了 OCT 层分割问题的克拉美-罗下限 (CRLB)。在推导 CRLB 时，我们解决了定义最佳统计模型的重要问题，这些模型最能代表视网膜各层的强度分布。此外，我们还计算了最优仿射偏差下的边界，反映了许多分割算法中对先验知识的利用。使用商业谱域 OCT 系统的人类视网膜活体图像进行了实验，显示了自动分割准确性提高的潜力。我们的通用数学模型可以很容易地适应几乎任何 OCT 系统。此外，本文中开发的信号和噪声统计模型可用于未来改进 OCT 图像去噪、重建和许多其他应用。
