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You Should Use Regression to Detect Cells


Kainz, Philipp; Urschler, Martin; Schulter, Samuel; Wohlhart, Paul; Lepetit, Vincent (2015). You Should Use Regression to Detect Cells. In: Navab, Nassir. Lecture Notes in Computer Science. Cham, 276-283.

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.

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.

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5 citations in Web of Science®
11 citations in Scopus®
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Additional indexing

Item Type:Book Section, not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2015
Deposited On:09 Feb 2016 15:56
Last Modified:08 Dec 2017 18:31
Additional Information:Chapter Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Publisher DOI:https://doi.org/10.1007/978-3-319-24574-4_33

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