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Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images

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Abstract

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.

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Funding

This work is supported by the Instituto de Salud Carlos III, Government of Spain, and FEDER funds through the DTS18/00136 research project and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro de Investigación del Sistema Universitário de Galicia, Ref. ED431G 2019/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, University of A Coruña, 15071, A Coruña, Spain

    Joaquim de Moura, Jorge Novo & Marcos Ortega

  2. CITIC - Research Center of Information and Communication Technologies, University of A Coruña, 15071, A Coruña, Spain

    Joaquim de Moura, Jorge Novo & Marcos Ortega

  3. Faculty of Engineering, University of Porto, 4200-465, Porto, Portugal

    Gabriela Samagaio

  4. Department of Ophthalmology, Complejo Hospitalario Universitario de Santiago, 15706, Santiago de Compostela, Spain

    Pablo Almuina & María Isabel Fernández

  5. Instituto Oftalmológico Gómez-Ulla, 15706, Santiago de Compostela, Spain

    María Isabel Fernández

  6. University of Santiago de Compostela, 15705, Santiago de Compostela, Spain

    María Isabel Fernández

Authors
  1. Joaquim de Moura
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  2. Gabriela Samagaio
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  3. Jorge Novo
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  4. Pablo Almuina
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  5. María Isabel Fernández
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  6. Marcos Ortega
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Correspondence to Joaquim de Moura.

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de Moura, J., Samagaio, G., Novo, J. et al. Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images. J Digit Imaging 33, 1335–1351 (2020). https://doi.org/10.1007/s10278-020-00360-y

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