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We apply three optical coherence tomography (OCT) image analysis techniques to extract morphometric information from OCT images obtained on peripheral nerves of rat. The accuracy of each technique is evaluated against histological measurements accurate to +/-1 um. The three OCT techniques are: 1) average depth resolved profile (ADRP); 2) autoregressive spectral estimation (AR-SE); and, 3) correlation of the derivative spectral estimation (CoD-SE). We introduce a scanning window to the ADRP technique which provides transverse resolution, and improves epineurium thickness estimates - with the number of analysed images showing agreement with histology increasing from 2/10 to 5/10 (Kruskal-Wallis test, {alpha} = 0.05). A new method of estimating epineurium thickness, using the AR-SE technique, showed agreement with histology in 6/10 analysed images (Kruskal-Wallis test, {alpha} = 0.05). Using a tissue sample in which histology identified two fascicles with an estimated difference in mean fibre diameter of 4 um, the AR-SE and CoD-SE techniques both correctly identified the fascicle with larger fibre diameter distribution but incorrectly estimated the magnitude of this difference as 0.5um. The ability of OCT signal analysis techniques to extract accurate morphometric details from peripheral nerve is promising but restricted in depth by scattering in adipose and neural tissues.
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (
A sensation of fullness in the bladder is a regular experience, yet the mechanisms that act to generate this sensation remain poorly understood. This is an important issue because of the clinical problems that can result when this system does not fun
Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commerc
We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). By simulating only the end of the two arms of the interferometer and computing the interference signal in post
Purpose: To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for 1 eye of eac