Share Email Print
cover

Proceedings Paper

Spatially aware deep learning improves identification of retinal pigment epithelial cells with heterogeneous fluorescence levels visualized using adaptive optics
Author(s): Jianfei Liu; Yoo-Jean Han; Tao Liu; Johnny Tam
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Defects in retinal pigment epithelial (RPE) cells, which nourish retinal neurosensory photoreceptor cells, contribute to many blinding diseases. Recently, the combination of adaptive optics (AO) imaging with indocyanine green (ICG) has enabled the visualization of RPE cells directly in patients’ eyes, which makes it possible to monitor cellular status in real time. However, RPE cells visualized using AO-ICG have ambiguous boundaries and minimal intracellular contrast, making it difficult for computer algorithms to identify cells solely based on image appearance information. Here, we demonstrate the importance of providing spatial information for deep learning networks. We used a training dataset containing 1,633 AO images and a separate dataset containing 250 images for validation. Whereas the original LinkNet was unable to reliably identify low-contrast RPE cells, we found that our proposed spatially-aware LinkNet which has direct access to additional spatial information about the hexagonal arrangement of RPE cells (auxiliary spatial constraints) achieved better results. The overall precision, recall, and F1 score from the spatially aware deep learning method were 92.1±4.3%, 88.2±5.7%, and 90.0±3.8% (mean±SD) respectively, which was significantly better than the original LinkNet with 92.0±4.6%, 57.9±13.3%, and 70.0±10.6 (p<0.05). These experimental results demonstrate that the auxiliary spatial constraints are the key factor for improving RPE identification accuracy. Explicit incorporation of spatial constraints into existing deep learning networks may be useful for handling images with known spatial constraints and low image intensity information at cell boundaries.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131719 (28 February 2020); doi: 10.1117/12.2549290
Show Author Affiliations
Jianfei Liu, National Eye Institute, National Institutes of Health (United States)
Yoo-Jean Han, National Eye Institute, National Institutes of Health (United States)
Tao Liu, National Eye Institute, National Institutes of Health (United States)
Johnny Tam, National Eye Institute, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray