ArticleBiomedical optics express2018
Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Adaptive Optics Image Analysis Using Generative AI (GPT-4) Scripting: Exploratory Study.JMIR formative research · 2026Article
- Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.Biomedical optics express · 2024Article
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques.Frontiers in medicine · 2022Review
- Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images.IEEE transactions on medical imaging · 2021Article
- Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO).Progress in retinal and eye research · 2021Review
- Article
- Spatially Aware Dense-LinkNet Based Regression Improves Fluorescent Cell Detection in Adaptive Optics Ophthalmic Images.IEEE journal of biomedical and health informatics · 2020Article
- Review
- Microvasculature Segmentation and Intercapillary Area Quantification of the Deep Vascular Complex Using Transfer Learning.Translational vision science & technology · 2020Article
- Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks.Translational vision science & technology · 2019Article
- RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2019Article
- Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images.Journal of current ophthalmologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.
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