July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images
Author Affiliations & Notes
  • Harm van Zeeland
    EyeNED Research Group, Department of Radiology and Nuclear medicine, Radboud University Medical Center, Nijmegen, Netherlands
    Department of Ophthalmology, Radboud University Medical Center, Nijmegen, Netherlands
  • James Meakin
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, Netherlands
  • Bart Liefers
    EyeNED Research Group, Department of Radiology and Nuclear medicine, Radboud University Medical Center, Nijmegen, Netherlands
  • Cristina González-Gonzalo
    EyeNED Research Group, Department of Radiology and Nuclear medicine, Radboud University Medical Center, Nijmegen, Netherlands
  • Akshayaa Vaidyanathan
    EyeNED Research Group, Department of Radiology and Nuclear medicine, Radboud University Medical Center, Nijmegen, Netherlands
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, Netherlands
  • Caroline C W Klaver
    Ophthalmology & Epidemiology, Erasmus MC, Rotterdam, Netherlands
    Department of Ophthalmology, Radboud University Medical Center, Nijmegen, Netherlands
  • Clara I Sanchez
    EyeNED Research Group, Department of Radiology and Nuclear medicine, Radboud University Medical Center, Nijmegen, Netherlands
    Department of Ophthalmology, Radboud University Medical Center, Nijmegen, Netherlands
  • Footnotes
    Commercial Relationships   Harm van Zeeland, None; James Meakin, None; Bart Liefers, None; Cristina González-Gonzalo, None; Akshayaa Vaidyanathan, None; Bram van Ginneken, None; Caroline Klaver, None; Clara Sanchez, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 6118. doi:https://doi.org/
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      Harm van Zeeland, James Meakin, Bart Liefers, Cristina González-Gonzalo, Akshayaa Vaidyanathan, Bram van Ginneken, Caroline C W Klaver, Clara I Sanchez; EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images. Invest. Ophthalmol. Vis. Sci. 2019;60(9):6118. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Researchers and specialists in the field of ophthalmology currently rely on suboptimal vendor-specific software solutions for viewing and annotating retinal images. Our goal was to develop a fully-featured vendor-independent application that allows researchers and specialists to visualize multi-modal retinal images, perform spatial alignment and annotations, and review outputs of artificial intelligence (AI) algorithms.

Methods : The application consists of a web-based front-end that allows users to analyze baseline and follow-up images in a multi-modal viewer. It communicates with a back-end interface for grader authentication, loading and storing of images and annotation data. Several types of annotation techniques are available, ranging from image-level classification to point-based and region-based lesion-level annotations.

The user can select color fundus (CF) images, optical coherence tomography (OCT) volumes, infrared (IR) and autofluorescence (AF) images to be shown simultaneously in the viewer. Spatial alignment of the different modalities can be performed using an integrated affine registration method by clicking on corresponding landmarks, after which a synchronized cursor will appear. After several graders have annotated lesions, the application can be used to compare these and create a consensus grading.

Results : The application was used by graders and researchers in the EyeNED research group. Region based annotations of geographic atrophy were made for 313 studies containing 488 CF images and 68 OCT images; and of drusen in 100 OCT b-scans. Semi-automatic annotation of the area of central retinal atrophy in Stargardt disease was performed for 67 AF images. Point-based annotation was carried out on lesions in 50 CF images of diabetic retinopathy patients. The multimodal viewing and localisation of lesions was perceived as particularly helpful in the grading of lesions and consensus discussions.

Conclusions : A software solution has been developed to assist researchers and specialists to view and annotate retinal images. The application was successfully used for annotating lesions in various imaging modalities, facilitating the grading of images in large studies and the collection of annotations for AI solutions.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Screenshot showing a multi-modal hanging protocol with annotated lesions.

Screenshot showing a multi-modal hanging protocol with annotated lesions.

 

Screenshot showing the view for a consensus grading.

Screenshot showing the view for a consensus grading.

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