@inbook{b4eff37d63aa4e01b5c7ebe0ef898383,
title = "You Should Use Regression to Detect Cells",
abstract = "Automated cell detection in histopathology images is a hard problem due to the large variance of cell shape and appearance. We show that cells can be detected reliably in images by predicting, for each pixel location, a monotonous function of the distance to the center of the closest cell. Cell centers can then be identified by extracting local extremums of the predicted values. This approach results in a very simple method, which is easy to implement. We show on two challenging microscopy image datasets that our approach outperforms state-of-the-art methods in terms of accuracy, reliability, and speed. We also introduce a new dataset that we will make publicly available.",
author = "Philipp Kainz and Martin Urschler and Samuel Schulter and Paul Wohlhart and Vincent Lepetit",
year = "2015",
doi = "10.1007/978-3-319-24574-4_33",
language = "English",
isbn = "978-3-319-24573-7",
volume = "9351",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing AG ",
pages = "276--283",
editor = "Nassir Navab and Joachim Hornegger and Wells, {William M.} and Frangi, {Alejandro F.}",
booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015",
address = "Switzerland",
}