AMI

Background

Retinal diseases, such as age-related macular degeneration (AMD) or diabetic retinopathy (DR), are compromising the vision of more and more patients and causing blindness in an alarmingly large percentage of world population. Multimodal retinal imaging has become an indispensable diagnostic tool in Ophthalmology to early detect, treat and monitor these diseases and automated retina image analysis has the potential to improve the diagnostic process and make treatment monitoring more effective. However, efficiently leveraging information contained in multimodal imaging is a complex task and many traditional automated solutions do not suffice to accurately extract the required information. Specially, the manual assessment of quantitative temporal change for treatment decision of AMD suffers from observer dependence, and the joint assessment of different imaging techniques for one patient leave ambiguities in terms of spatial correspondence.

Geographic atrophy segmentation in CFI
Automatic segmentation of geographic atrophy in color fundus images.

Aim

We aim to develop solutions for the automated analysis of multimodal retinal imaging examinations in order to assess treatment response in patients with advanced wet AMD and provide treatment decision support to clinicians. For that, as an alternative to traditional techniques, deep learning algorithms will be developed that can accurately analyze and quantify changes in follow-up optical coherence tomography scans and multimodal retinal images. In this project, we will also integrate these algorithms in a platform tailored to provide an advanced visualization of these changes and the joint review of multimodal retinal images.

Lesions segmentation in OCT
Automatic segmentation of different lesions in optical coherence tomography.

Funding

AMI is a collaborative project of the Fraunhofer Gesellschaft and the Radboud University and University Medical Center.

Media

  • Best Demo Award from Live Demonstrations Workshop at SPIE Medical Imaging 2017 : “A Multimodal Workstation for Analysis of Retinal Images”

People

Bart Liefers

Bart Liefers

PhD student

Clarisa Sánchez

Clarisa Sánchez

Associate professor

Publications

  • B. Liefers, J. Colijn, C. González-Gonzalo, T. Verzijden, P. Mitchell, C. Hoyng, B. van Ginneken, C. Klaver and C. Sánchez. "A deep learning model for segmentation of geographic atrophy to study its long-term natural history", arXiv:1908.05621, 2019. Abstract ARXIV
  • B. Liefers, C. González-Gonzalo, C. Klaver, B. van Ginneken and C. Sánchez. "Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography", Medical Imaging with Deep Learning, 2019. Abstract/PDF URL
  • B. Liefers, J. Colijn, C. González-Gonzalo, A. Vaidyanathan, H. van Zeeland, P. Mitchell, C. Klaver and C. Sánchez. "Prediction of areas at risk of developing geographic atrophy in color fundus images using deep learning", Association for Research in Vision and Ophthalmology, 2019. Abstract
  • B. Liefers, F. Venhuizen, T. Theelen, C. Hoyng, B. van Ginneken and C. Sánchez. "Fovea Detection in Optical Coherence Tomography using Convolutional Neural Networks", Medical Imaging, 2017. Abstract/PDF DOI
  • B. Liefers, F. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, T. Theelen and C. Sánchez. "Automatic detection of the foveal center in optical coherence tomography", Association for Research in Vision and Ophthalmology, 2017. Abstract
  • B. Liefers, F. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen and C. Sánchez. "Automatic detection of the foveal center in optical coherence tomography", Biomedical Optics Express, 2017. Abstract/PDF DOI PMID URL