You Should Use Regression to Detect Cells

Philipp Kainz, Martin Urschler, Samuel Schulter, Paul Wohlhart, Vincent Lepetit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/Bericht

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.
Originalspracheenglisch
TitelMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Untertitel18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III
Redakteure/-innenNassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi
Herausgeber (Verlag)Springer International Publishing AG
Seiten276-283
Band9351
ISBN (elektronisch)978-3-319-24574-4
ISBN (Print)978-3-319-24573-7
DOIs
PublikationsstatusVeröffentlicht - 2015

Publikationsreihe

NameLecture Notes in Computer Science

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Kooperationen

  • BioTechMed-Graz

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