We acquired depth-resolved light scattering measurements from the retinas of triple transgenic Alzheimer's Disease (3xTg-AD) mice and wild type (WT) age-matched controls using co-registered angle-resolved low-coherence interferometry (a/LCI) and optical coherence tomography (OCT). Angleresolved light scattering measurements were acquired from the nerve fiber layer, outer plexiform layer, and retinal pigmented epithelium using image guidance and segmented thicknesses provided by coregistered OCT B-scans. Analysis of the OCT images showed a statistically significant thinning of the nerve fiber layer in AD mouse retinas compared to WT controls. The a/LCI scattering measurements provided complementary information that distinguishes AD mice by quantitatively characterizing tissue heterogeneity. The AD mouse retinas demonstrated higher mean and variance in nerve fiber layer light scattering intensity compared to WT controls. Further, the difference in tissue heterogeneity was observed through short-range spatial correlations that show greater slopes at all layers of interest for AD mouse retinas compared to Wt controls. A greater slope indicates a faster loss of spatial correlation, suggesting a loss of tissue self-similarity characteristic of heterogeneity consistent with AD pathology. Use of this combined modality introduces unique tissue texture characterization to complement development of future AD biomarker analysis.

Optical coherence tomography (OCT) is used for diagnosis of esophageal diseases such as Barrett’s esophagus. Given the large volume of OCT data acquired, automated analysis is needed. Here we propose a bilateral connectivity-based neural network for in vivo human esophageal OCT layer segmentation. Our method, connectivity-based CE-Net (Bicon-CE), defines layer segmentation as a combination of pixel connectivity modeling and pixel-wise tissue classification. Bicon-CE outperformed other widely used neural networks and reduced common topological prediction issues in tissues from healthy patients and from patients with Barrett’s esophagus. This is the first end-to-end learning method developed for automatic segmentation of the epithelium in in vivo human esophageal OCT images.

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