Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images
| Autor: | María Isabel Fernández, Gabriela Samagaio, Jorge Novo, Marcos Ortega, Joaquim de Moura, Pablo Almuina |
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| Rok vydání: | 2020 |
| Předmět: |
genetic structures
Computer science Diabetic macular edema Visual Acuity Context (language use) Macular Edema 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Sørensen–Dice coefficient Optical coherence tomography Cut medicine Diabetes Mellitus Humans Radiology Nuclear Medicine and imaging Segmentation Macular edema Original Paper Diabetic Retinopathy Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Retinal Detachment Pattern recognition medicine.disease Computer Science Applications Computer-aided diagnosis Artificial intelligence business 030217 neurology & neurosurgery Tomography Optical Coherence |
| Zdroj: | J Digit Imaging |
| ISSN: | 1618-727X |
| Popis: | The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identification and precise delimitation. In the case of the DRT edemas, given their fuzzy regional appearance that requires a complex extraction process, an exhaustive analysis using a learning strategy was designed, exploiting intensity, texture, and clinical-based information. The different steps of this methodology were validated with a heterogeneous set of 262 OCT images, using the manual labeling provided by an expert clinician. In general terms, the system provided satisfactory results, reaching Dice coefficient scores of 0.8768, 0.7475, and 0.8913 for the segmentation of SRD, CME, and DRT edemas, respectively. |
| Databáze: | OpenAIRE |
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