Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks - 115110H
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Document typeConference report
Defense date2020
PublisherInternational Society for Photo-Optical Instrumentation Engineers (SPIE)
Rights accessRestricted access - publisher's policy
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Abstract
Automated cell counting in in-vivo specular microscopy images is challenging, especially in situations where single-cell segmentation methods fail due to pathological conditions. This work aims to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. We cast the problem of cell segmentation as a supervised multi-class segmentation problem. The goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, indicating healthy (cells) and pathological regions (e.g., guttae). We trained a U-net model by extracting 96 96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a physician. Encouraging results show that the proposed method can deliver reliable feature segmentation enabling more accurate cell density estimations for assessing the state of the cornea.
CitationSierra, J. [et al.]. Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks - 115110H. A: SPIE Optical Engineering + Applications. "Applications of Machine Learning 2020 : 24 August-4 September 2020, online only, United States". Washington: International Society for Photo-Optical Instrumentation Engineers (SPIE), 2020, p. 1-6. ISBN 978-1-5106-3828-0. DOI 10.1117/12.2569258.
ISBN978-1-5106-3828-0
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Sierra_cornea guttata_Proc SPIE 11511_2020.pdf | Proceeding publicado | 3,313Mb | Restricted access |