Automatic segmentation of hyperreflective foci in OCT images. (September 2019)

• Record Type: Journal Article
• Authors: Varga, László; Kovács, Attila; Grósz, Tamás; Thury, Géza; Hadarits, Flóra; Dégi, Rózsa; Dombi, József
• Is Part Of: Computer methods and programs in biomedicine. Volume 178(2019)
• Page Start: 91
• Page End: 103
• Publication Date: 2019-09
• Subjects: OCT -- HF segmentation -- Deep neural network -- Convolutional network -- Image processing -- GPGPU; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.06.019
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The leading cause of vision loss in the Western World is Age-related Macular Degeneration. The diagnosis of AMD is commonly done by the analyzing biomarkers on OCT images such as Hyperreflective Foci (HF). We proposed a method for training DNN-s for the automatic segmentation of HF. Automatic segmentation methods perform well on clinical data. Abstract: Background and Objective: The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern medicines, tracking the number of Hyperreflective Foci (HF) on Optical Coherence Tomography (OCT) images should assist the treatment of patients. Here, we developed a framework based on deep learning for the automatic segmentation of HF in OCT images. Methods: We collected OCT images and annotated them, then these images underwent image preprocessing, and feature extraction steps. Using the prepared data we trained different types of Conventional-, Deep- and Convolutional Neural Networks to perform the task of the automatic segmentation of HF. Results: We evaluated the various Neural Networks, by performing HF segmentation of clinical data belonging to patients, whose data were excluded from the training process. The results suggest that our systems can achieve reasonably high Dice Coefficient values, and they are comparable with (i.e., in most cases above 95%) the similarity between manual annotations performed by different physicians. Conclusion: From the results, it can be concluded that neural networks can be used to accurately segment HF in OCT images. The results are sufficiently accurate for us to incorporate them into the next phase of the research, building a decision support system for everyday clinical practice.