Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks
| Autor: | Majda Hadziahmetovic, Ying Xu, Miroslav Pajic, Qitong Gao, Scott W. Cousins, Joshua Amason, Anna Loksztejn |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
retina Computer science Confocal media_common.quotation_subject Feature extraction Biomedical Engineering Retinal Pigment Epithelium law.invention 03 medical and health sciences Mice 0302 clinical medicine Confocal microscopy law medicine Contrast (vision) Animals media_common Retina Retinal pigment epithelium Microscopy Confocal business.industry Special Issue Choroid Deep learning deep learning Pattern recognition Ophthalmology 030104 developmental biology medicine.anatomical_structure 030221 ophthalmology & optometry Artificial intelligence sense organs RPE Neural Networks Computer business |
| Zdroj: | Translational Vision Science & Technology |
| ISSN: | 2164-2591 |
| Popis: | Purpose To develop a neural network (NN)-based approach, with limited training resources, that identifies and counts the number of retinal pigment epithelium (RPE) cells in confocal microscopy images obtained from cell culture or mice RPE/choroid flat-mounts. Methods Training and testing dataset contained two image types: wild-type mice RPE/choroid flat-mounts and ARPE 19 cells, stained for Rhodamine-phalloidin, and imaged with confocal microscopy. After image preprocessing for denoising and contrast adjustment, scale-invariant feature transform descriptors were used for feature extraction. Training labels were derived from cells in the original training images, annotated and converted to Gaussian density maps. NNs were trained using the set of training input features, such that the obtained NN models accurately predicted corresponding Gaussian density maps and thus accurately identifies/counts the cells in any such image. Results Training and testing datasets contained 229 images from ARPE19 and 85 images from RPE/choroid flat-mounts. Within two data sets, 30% and 10% of the images, were selected for validation. We achieved 96.48% ± 6.56% and 96.88% ± 3.68% accuracy (95% CI), on ARPE19 and RPE/choroid flat-mounts. Conclusions We developed an NN-based approach that can accurately estimate the number of RPE cells contained in confocal images. Our method achieved high accuracy with limited training images, proved that it can be effectively used on images with unclear and curvy boundaries, and outperformed existing relevant methods by decreasing prediction error and variance. Translational relevance This approach allows efficient and effective characterization of RPE pathology and furthermore allows the assessment of novel therapeutics. |
| Databáze: | OpenAIRE |
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