Spatially Aware Dense-LinkNet Based Regression Improves Fluorescent Cell Detection in Adaptive Optics Ophthalmic Images.
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- Record sourced from PubMed, PMID 32750947.
- Also identified by DOI 10.1109/JBHI.2020.3004271 and PMC identifier 7754796.
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Abstract
Retinal pigment epithelial (RPE) cells play an important role in nourishing retinal neurosensory photoreceptor cells, and numerous blinding diseases are associated with RPE defects. Their fluorescence signature can now be visualized in the living human eye using adaptive optics (AO) imaging combined with indocyanine green (ICG), which motivates us to develop an automated RPE detection method to improve the quantitative evaluation of RPE status in patients. This paper proposes a spatially-aware, Dense-LinkNet-based regression approach to improve the detection of in vivo fluorescent cell patterns, achieving precision, recall, and F1-Score of 93.6 ± 4.3%, 81.4 ± 9.5%, and 86.7 ± 5.7%, respectively. These results demonstrate the utility of incorporating spatial inputs into a deep learning-based regression framework for cell detection.
Medical subject headings
- Deep Learning
- Fluorescent Dyes
- Image Processing, Computer-Assisted
- Optical Imaging
- Retinal Pigment Epithelium