Stochastic Differential Equations for Automatic Quality Control of Retinal Optical Coherence Tomography Images Publisher Pubmed

Summary: A study developed AI-based tools to spot poor-quality eye scans, helping doctors get more reliable results for vision care. #EyeHealth #MedicalAI

Tajmirriahi M¹ ; Rostamian R¹ ; Amini Z¹ ; Hamidi A² ; Zam A³ ; Rabbani H¹
Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Published:2022

Abstract

Optical coherence tomography is widely used to provide high resolution images from retina. During data acquisition, several artifacts may be associated with OCT images which clearly remove information of retinal layers and degrade the quality of images. Manual assessment of the acquired OCT images is hard and time consuming. Therefore, an automatic quality control step is necessary to detect poor images for next decisions of eliminating them and even re-scanning. In this study, a novel automatic quality control methodology is proposed for early assessment of the OCT images quality by employing stochastic differential equations (SDE). In this method -stable nature of OCT images is represented by applying a fractional Laplacian filter and parameters of the obtained -stable are fed to an SVM to automatically detect high quality vs poor quality images. The simulation results on a large dataset of normal and abnormal OCT images show that proposed method has outstanding performance in detection of poor vs high quality images. The methodology is applicable to the image quality assessment of other OCT scanning devices as well. Clinical Relevance - Automatic quality control assessment of retinal OCT images provides reliable data for diagnosis of retinal and systematic diseases in clinical applications. © 2022 IEEE.