1. Unbiased corneal tissue analysis using Gabor-domain optical coherence microscopy and machine learning for automatic segmentation of corneal endothelial cells
- Author
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Cristina Canavesi, Andrea Cogliati, and Holly B. Hindman
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Paper ,Image quality ,Computer science ,medicine.medical_treatment ,Biomedical Engineering ,Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics ,Machine learning ,computer.software_genre ,Convolutional neural network ,Cornea ,Machine Learning ,Biomaterials ,Optical coherence tomography ,Microscopy ,medicine ,Humans ,corneal imaging ,Corneal transplantation ,optical coherence tomography ,medicine.diagnostic_test ,business.industry ,Endothelium, Corneal ,Endothelial Cells ,Gold standard (test) ,Image segmentation ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,ophthalmology ,medicine.anatomical_structure ,Artificial intelligence ,eye banking ,business ,computer - Abstract
Significance: An accurate, automated, and unbiased cell counting procedure is needed for tissue selection for corneal transplantation. Aim: To improve accuracy and reduce bias in endothelial cell density (ECD) quantification by combining Gabor-domain optical coherence microscopy (GDOCM) for three-dimensional, wide field-of-view (1 mm2) corneal imaging and machine learning for automatic delineation of endothelial cell boundaries. Approach: Human corneas stored in viewing chambers were imaged over a wide field-of-view with GDOCM without contacting the specimens. Numerical methods were applied to compensate for the natural curvature of the cornea and produce an image of the flattened endothelium. A convolutional neural network (CNN) was trained to automatically delineate the cell boundaries using 180 manually annotated images from six corneas. Ten additional corneas were imaged with GDOCM and compared with specular microscopy (SM) to determine performance of the combined GDOCM and CNN to achieve automated endothelial counts relative to current procedural standards. Results: Cells could be imaged over a larger area with GDOCM than SM, and more cells could be delineated via automatic cell segmentation than via manual methods. ECD obtained from automatic cell segmentation of GDOCM images yielded a correlation of 0.94 (p
- Published
- 2020
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