Automatically Enhanced OCT Scans of the Retina: A proof of concept study.
Stefanos ApostolopoulosJazmín SalasJosé L P OrdóñezShern Shiou TanCarlos CillerAndreas EbneterMartin Sebastian ZinkernagelRaphael SznitmanSebastian WolfSandro De ZanetMarion R MunkPublished in: Scientific reports (2020)
In this work we evaluated a postprocessing, customized automatic retinal OCT B-scan enhancement software for noise reduction, contrast enhancement and improved depth quality applicable to Heidelberg Engineering Spectralis OCT devices. A trained deep neural network was used to process images from an OCT dataset with ground truth biomarker gradings. Performance was assessed by the evaluation of two expert graders who evaluated image quality for B-scan with a clear preference for enhanced over original images. Objective measures such as SNR and noise estimation showed a significant improvement in quality. Presence grading of seven biomarkers IRF, SRF, ERM, Drusen, RPD, GA and iRORA resulted in similar intergrader agreement. Intergrader agreement was also compared with improvement in IRF and RPD, and disagreement in high variance biomarkers such as GA and iRORA.
Keyphrases
- optical coherence tomography
- optic nerve
- diabetic retinopathy
- computed tomography
- neural network
- image quality
- pet ct
- deep learning
- dual energy
- air pollution
- magnetic resonance
- contrast enhanced
- machine learning
- quality improvement
- clinical practice
- age related macular degeneration
- high intensity
- immune response
- data analysis