Search results for "SD-OCT"
showing 4 items of 4 documents
Glaukomas skrīnings
2017
Darba mērķis. Noskaidrot pirmreizējā pieraksta glaukomas riska grupas pacientu biežumu, to subjektīvos un objektīvos datus. Darba metodes. Pētījumā tika iekļauti pirmreizējā pieraksta pacienti kuri sasnieguši vismaz 40 gadu vecumu un nav datu par glaukomu anamnēzē. Kā skrīninga metode tika izmantota optiskā koherentā tomogrāfija un citi izmeklējumi (bezkontakta tonometrs, autorefraktometrs), kā arī 15 jautājumu anketa, lai noskaidrotu glaukomas riska faktorus un diagnozes varbūtību. Rezultāti. No pētījumā iekļautajiem 50 pacientiem, asimptomātiska glaukoma tika konstatēta 4% pacientu (n=2) un 2% pacientu (n=1) aizdomas par glaukomu. Glaukomas pacientiem statistiski ticami biežāk bija vīrieš…
High myopic patients with and without foveoschisis: morphological and functional characteristics.
2020
Purpose: Myopic foveoschisis (MF) is characterized by the splitting of the retinal layers in the fovea of patients with high myopia (HM). MF may progress into foveal detachment or macular hole formation with consequent loss of central vision. The aim of this study is to investigate morphological and functional changes of the macular region in myopic subjects with and without foveoschisis. Design: Observational, cross-sectional, comparative study. Methods: Forty-eight patients with HM and 24 healthy controls were evaluated by spectral domain-optical coherence tomography (SD-OCT), multifocal electroretinography (mfERG) and microperimetry (MP-1) tests to assess macular thickness, functionality…
Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach
2016
International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…
Classifying DME vs Normal SD-OCT volumes: A review
2016
International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this comm…