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Bortsova, G. ; Sterr, M. ; Wang, L. ; Milletari, F.* ; Navab, N.* ; Böttcher, A. ; Lickert, H. ; Theis, F.J. ; Peng, T.

Mitosis detection in intestinal crypt images with hough forest and conditional random fields.

Lect. Notes Comput. Sc. 10019, 287-295 (2016)
Postprint DOI Verlagsversion bestellen
Open Access Green
Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understood. Asymmetric stem cell divisions have been linked to intestinal stem cell homeostasis and secretory fate commitment. We monitored cell divisions using 4D live cell imaging of cultured intestinal crypts to characterize division modes by means of measurable features such as orientation or shape. A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually. To assist data processing, we developed a learning based method to automatically detect mitosis events. The method contains a dual-phase framework for joint detection of dividing cells (mothers) and their progeny (daughters). In the first phase we detect mother and daughters independently using Hough Forest whilst in the second phase we associate mother and daughters by modelling their joint probability as Conditional Random Field (CRF). The method has been evaluated on 32 movies and has achieved an AUC of 72%, which can be used in conjunction with manual correction and dramatically speed up the processing pipeline.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Conditional Random Field ; Hough Forest ; Mitosis Detection
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 978-3-319-47156-3
Bandtitel Machine Learning in Medical Imaging
Quellenangaben Band: 10019, Heft: , Seiten: 287-295 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]