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.
Automatic segmentation of geographic atrophy in color fundus images.
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.
Automatic segmentation of different lesions in optical coherence tomography.
AMI is a collaborative project of the Fraunhofer Gesellschaft and the Radboud University and University Medical Center.
- Best Demo Award from Live Demonstrations Workshop at SPIE Medical Imaging 2017 : “A Multimodal Workstation for Analysis of Retinal Images”