Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks

Juan S. Sierra; Jesus Pineda; Eduardo Viteri; Daniela Rueda; Beatriz Tibaduiza; Rúben D. Berrospi; Alejandro Tello; Virgilio Galvis; Giovanni Volpe; María S. Millán; Lenny A. Romero; Andrés G. Marrugo

doi:10.1117/12.2569258

19 August 2020 Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks

Juan S. Sierra, Jesus Pineda, Eduardo Viteri, Daniela Rueda, Beatriz Tibaduiza, Rúben D. Berrospi, Alejandro Tello, Virgilio Galvis, Giovanni Volpe, María S. Millán, Lenny A. Romero, Andrés G. Marrugo

Author Affiliations +

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110H (2020) https://doi.org/10.1117/12.2569258
Event: SPIE Optical Engineering + Applications, 2020, Online Only

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.

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Juan S. Sierra, Jesus Pineda, Eduardo Viteri, Daniela Rueda, Beatriz Tibaduiza, Rúben D. Berrospi, Alejandro Tello, Virgilio Galvis, Giovanni Volpe, María S. Millán, Lenny A. Romero, and Andrés G. Marrugo "Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110H (19 August 2020); https://doi.org/10.1117/12.2569258

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