Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images. (March 2023)

• Record Type: Journal Article
• Authors: López-Varela, Emilio; de Moura, Joaquim; Novo, Jorge; Fernández-Vigo, José Ignacio; Moreno-Morillo, Francisco Javier; Ortega, Marcos
• Is Part Of: Computerized medical imaging and graphics. Volume 104(2023)
• Publication Date: 2023-03
• Subjects: OCTA imaging -- Choriocapillaris -- Photodynamic Therapy -- Central Serous Chorioretinopathy -- Flow voids; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102172
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging. Graphical Abstract: ga1 Highlights: A novel methodology for the FVs segmentation in CC OCTA images. An efficient deformable registration methodology based on unsupervised CNN learning. Improving the efficacy of the FVs as a biomarker, helping to monitor the patient effectively. Monitoring photodynamic therapy treatment of patients with chronic CSC.