@inproceedings{fbf5fe2347dc4dae9c08f6f4a091610c,
title = "Deep convolutional neural networks for human embryonic cell counting",
abstract = "We address the problem of counting cells in time-lapse microscopy images of developing human embryos. Cell counting is considered as an important step in analyzing biological phenomenon such as embryo viability. Traditional approaches to counting cells rely on hand crafted features and cannot fully take advantage of the growth in data set sizes. In this paper, we propose a framework to automatically count the number of cells in developing human embryos. The framework employs a deep convolutional neural network model trained to count cells from raw microscopy images. We demonstrate the effectiveness of our approach on a data set of 265 human embryos. The results show that the proposed framework provides robust estimates of the number of cells in a developing embryo up to the 5-cell stage (i.e., 48 h post fertilization).",
author = "Aisha Khan and Stephen Gould and Mathieu Salzmann",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; Computer Vision - ECCV 2016 Workshops, Proceedings ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46604-0_25",
language = "English",
isbn = "9783319466033",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "339--348",
editor = "Gang Hua and Herv{\'e} J{\'e}gou",
booktitle = "Computer Vision - ECCV 2016 Workshops, Proceedings",
address = "Germany",
}