@inproceedings{197a24d445d242ac88c711f4e65382dd,
title = "Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks",
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. ",
keywords = "Convolutional neural network, Cornea guttata, Corneal endothelium, Medical image segmentation, Specular microscopy, U-net",
author = "Sierra, {Juan S.} and Jesus Pineda and Eduardo Viteri and Daniela Rueda and Beatriz Tibaduiza and Berrospi, {R{\'u}ben D.} and Alejandro Tello and Virgilio Galvis and Giovanni Volpe and Mill{\'a}n, {Mari{\'a} S.} and Romero, {Lenny A.} and Marrugo, {Andr{\'e}s G.}",
note = "Publisher Copyright: {\textcopyright} 2020 COPYRIGHT SPIE.; Applications of Machine Learning 2020 ; Conference date: 24-08-2020 Through 04-09-2020",
year = "2020",
doi = "10.1117/12.2569258",
language = "Ingl{\'e}s",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zelinski, {Michael E.} and Taha, {Tarek M.} and Jonathan Howe and Awwal, {Abdul A.} and Iftekharuddin, {Khan M.}",
booktitle = "Applications of Machine Learning 2020",
}