Foveal Avascular Zone Segmentation Using Deep Learning-Driven Image-Level Optimization and Fundus Photographs

The foveal avascular zone (FAZ) is a retinal area devoid of capillaries and associated with multiple retinal pathologies and visual acuity. Optical Coherence Tomography Angiography (OCT-A) is a very effective means of visualizing retinal vascular and avascular areas, but its use remains limited to r...

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Bibliographic Details
Published in:	2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Vol. 2023; pp. 1 - 5
Main Authors:	Coronado, I., Pachade, S., Dawoodally, H., Marioni, S. Salazar, Yan, J., Abdelkhaleq, R., Bahrainian, M., Jagolino-Cole, A., Channa, R., Sheth, S. A., Giancardo, L.
Format:	Conference Proceeding Journal Article
Language:	English
Published:	United States IEEE 01-04-2023
Subjects:	active contours Angiography convolutional neural networks deep learning foveal avascular zone fundus photos Image segmentation Pathology Photography Sociology Training Visualization deep learning active contours convolutional neural networks foveal avascular zone fundus photos
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Summary:	The foveal avascular zone (FAZ) is a retinal area devoid of capillaries and associated with multiple retinal pathologies and visual acuity. Optical Coherence Tomography Angiography (OCT-A) is a very effective means of visualizing retinal vascular and avascular areas, but its use remains limited to research settings due to its complex optics limiting availability. On the other hand, fundus photography is widely available and often adopted in population studies. In this work, we test the feasibility of estimating the FAZ from fundus photos using three different approaches. The first two approaches rely on pixel-level and image-level FAZ information to segment FAZ pixels and regress FAZ area, respectively. The third is a training mask-free pipeline combining saliency maps with an active contours approach to segment FAZ pixels while being trained on image-level measures of the FAZ areas. This enables training FAZ segmentation methods without manual alignment of fundus and OCT-A images, a time-consuming process, which limits the dataset that can be used for training. Segmentation methods trained on pixel-level labels and image-level labels had good agreement with masks from a human grader (respectively DICE of 0.45 and 0.4). Results indicate the feasibility of using fundus images as a proxy to estimate the FAZ when angiography data is not available.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1945-7928 1945-8452
DOI:	10.1109/ISBI53787.2023.10230410