Gabriela Samagaio1, Aída Estévez2, Joaquim de Moura3, Jorge Novo4, María Isabel Fernández5, Marcos Ortega6. 1. Department of Computing, University of A Coruña, A Coruña, Spain. Electronic address: gabriela.samagaio@udc.es. 2. Department of Ophthalmology, Complejo Hospitalario, Universitario de Santiago, Santiago de Compostela, Spain. Electronic address: aidaestevez55@gmail.com. 3. Department of Computing, University of A Coruña, A Coruña, Spain; CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain. Electronic address: joaquim.demoura@udc.es. 4. Department of Computing, University of A Coruña, A Coruña, Spain; CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain. Electronic address: jnovo@udc.es. 5. Instituto Oftalmológico Gómez-Ulla, Santiago de Compostela, Spain; Department of Ophthalmology, Complejo Hospitalario, Universitario de Santiago, Santiago de Compostela, Spain; University of Santiago de Compostela, Santiago de Compostela, Spain. Electronic address: maribelfernandez@institutogomez-ulla.es. 6. Department of Computing, University of A Coruña, A Coruña, Spain; CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain. Electronic address: mortega@udc.es.
Abstract
BACKGROUND AND OBJECTIVE: The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments. METHODS: This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence. RESULTS: The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset. CONCLUSIONS: The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.
BACKGROUND AND OBJECTIVE: The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments. METHODS: This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence. RESULTS: The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset. CONCLUSIONS: The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.