视网膜图像的算法统计学

中文摘要 AI 辅助

已有大量图像处理与机器学习研究，用于从活体视网膜光学相干断层扫描（OCT）成像中测量和分类疾病进展。本文研究的图像体积大、结构复杂且为三维（3-D），难以有效可视化。当前许多监督机器学习方法（如神经网络）属于非度量方法，其生成的任何特征或测量值都可能引入系统失真，该失真或与潜在无意义的生理差异相关。本文提出一种结合归一化压缩距离（NCD）与各向异性结构增强滤波器的度量学习方法，用于量化和可视化一组三维视网膜图像间的主要差异。我们将NCD测量的图像对结构差异与医生测量的视野功能变化进行验证，达到约0.5 dB的预测误差，比非度量深度学习方法更准确。本文提出归一化压缩向量（NCV）作为测量一组三维显微图像间视觉差异的特征集，通过对一名中度非进展性青光眼患者及通过眼压设置操纵的非人灵长类动物模型的应用，证明了NCV在可视化和测量变化模式方面的效用。最后我们简要模拟了非度量嵌入特征（如神经网络生成的特征）引入的与类别相关的统计失真。

英文摘要

There has been a tremendous amount of image processing and machine learning research to measure and classify disease progression from live optical coherence tomography (OCT) imaging of the retina. The images considered here are large, complex, three-dimensional (3-D) and difficult to visualize effectively. Many current supervised machine learning approaches, \emph{e.g.} neural networks, are non-metric meaning that any features or measurements generated can introduce systematic distortion that may be correlated with underlying non-meaningful physiological differences. Here we present a metric learning approach using the normalized compression distance (NCD) combined with anisotropic structure-enhancing filters to quantify and visualize the principal differences among a collection of 3-D retinal images. We validate the NCD-measured structural differences between pairs of images against the physician-measured change in visual field function, achieving a prediction error of $\sim$ 0.5 dB, more accurate than non-metric deep learning approaches. The normalized compression vectors (NCV) are proposed as a feature set measuring visual differences among a collection of 3-D microscopy images. The utility of the NCV for visualizing and measuring patterns of change is demonstrated for a human with moderate non-progressing glaucoma and for a non-human primate model using intraocular pressure setting manipulation. We conclude with a brief simulation of non-metric embedding features, \emph{e.g.} from neural networks, introducing class-correlated statistical distortion.

发表机构

• Drexel University（德雷塞尔大学）
• Wills Eye Hospital（威尔斯眼科医院）
• Thomas Jefferson University（托马斯杰斐逊大学）

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