@inproceedings{e201d7730cae47e09840ec5145724e31,
title = "Automatic segmentation of corneal endothelium images with convolutional neural network",
abstract = "A fully-automatic segmentation of corneal endothelial images is addressed in this paper. It can find its application in the medicine removing the burden of manual annotations from the physicians allowing for faster patient diagnosis. The proposed system is based on pre-trained convolutional neural network AlexNet and uses a transfer learning methodology to build a system for delineation of endothelial cells. The training is based on the classification of small patches of an image which represents cell body or cell border class. The validation set proved that 99\% correct classification ratio accuracy and F1 score were achieved. Exploiting this network in a system configured for segmentation it proved very good detection of cell bodies and supported by best-fit skeletonization allowed to locate cell borders precisely.",
keywords = "Classification, Convolutional neural network, Corneal endothelium images, Segmentation",
author = "Karolina Nurzynska",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 14th International Conference on Beyond Databases, Architectures and Structures, BDAS 2018 Held at the 24th IFIP World Computer Congress, WCC 2018 ; Conference date: 18-09-2018 Through 20-09-2018",
year = "2018",
doi = "10.1007/978-3-319-99987-6\_25",
language = "English",
isbn = "9783319999869",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "323--333",
editor = "Stanislaw Kozielski and Dariusz Mrozek and Pawel Kasprowski and Bozena Malysiak-Mrozek and Daniel Kostrzewa",
booktitle = "Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety - 14th International Conference, BDAS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Proceedings",
address = "Germany",
}