網膜画像のアルゴリズム統計解析
Algorithmic statistics of retinal images
OCT網膜画像の構造差を正規化圧縮距離（NCD）と異方性フィルタで定量化し、医師の視野測定値と照合して予測誤差約0.5dBを達成。非計量な深層学習より高精度で、緑内障進行の可視化にも応用した。
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著者: Loan Huynh, Ronald Zambrano, Layton Aho, Fabio Lavinsky, Gadi Wollstein, Joel S. Schuman, Andrew R. Cohen
分類: eess.IV, cs.CV
原文アブストラクト
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.