Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks
2019
Purpose: To develop a fully automatic method, based on deep learning algorithms,
for determining the locations of cone photoreceptors within adaptive optics scanning
laser ophthalmoscope images and evaluate its performance against a dataset of
manually segmented images. Methods: A fully convolutional network (FCN) based on U-Net architecture was used
to generate prediction probability maps and then used a localization algorithm to
reduce the prediction map to a collection of points. The proposed method was
trained and tested on two publicly available datasets of different imaging modalities,
with Dice overlap, false discovery rate, and true positive reported to assess
performance. Results: The proposed method achieves a Dice coefficient of 0.989, true positive rate
of 0.987, and false discovery rate of 0.009 on the first confocal dataset; and a Dice
coefficient of 0.926, true positive rate of 0.909, and false discovery rate of 0.051 on the
second split detector dataset. Results compare favorably with a previously proposed
method, but this method provides quicker (25 times faster) evaluation performance. Conclusions: The proposed FCN-based method demonstrates that deep learning
algorithms can achieve accurate cone localizations, almost comparable to a human
expert, while labeling the images. Translational Relevance: Manual cone photoreceptor identification is a timeconsuming
task due to the large number of cones present within a single image;
using the proposed FCN-based method could support the image analysis task,
drastically reducing the need for manual assessment of the photoreceptor mosaic.
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