An Extensive-Form Game Paradigm for Visual Field Testing via Deep Reinforcement Learning.

Glaucoma is the leading cause of irreversible but preventable blindness worldwide, and visual field testing is an important tool for its diagnosis and monitoring. Testing using standard visual field thresholding procedures is time-consuming, and prolonged test duration leads to patient fatigue and decreased test reliability. Different visual field testing algorithms have been developed to shorten testing time while maintaining accuracy.

RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning.

Purpose: The purpose of this study was to develop a deep learning-based fully automated reconstruction and quantification algorithm which automatically delineates the neurites and somas of retinal ganglion cells (RGCs).

Methods: We trained a deep learning-based multi-task image segmentation model, RGC-Net, that automatically segments the neurites and somas in RGC images. A total of 166 RGC scans with manual annotations from human experts were used to develop this model, whereas 132 scans were used for training, and the remaining 34 scans were reserved as testing data.

Labeled quantitative proteomics dataset of optogenetics induced axon regeneration in mice.

This labeled quantitative proteomics dataset was collected from a transgenic channel rhodopsin mouse model (Chr2) subjected to light stimulation after traumatic optic nerve crush (ONC). Protein extraction was performed by careful mincing of the tissue in extraction buffer (TEAB, NaCl and SDS). Protein amounts were normalized across samples using dot blot densitometry and ImageJ software.