Mitosis detection in intestinal crypt images with Hough forest and Conditional Random Fields

Bortsova G, Sterr M, Wang L, Milletari F, Navab N, Boettcher A, Lickert H, Theis F, Peng T (2016)

Publication Type: Conference contribution

Publication year: 2016

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10019 LNCS

Pages Range: 287-295

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Athens, GRC

ISBN: 9783319471563

DOI: 10.1007/978-3-319-47157-0_35

Abstract

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Bortsova, G., Sterr, M., Wang, L., Milletari, F., Navab, N., Boettcher, A.,... Peng, T. (2016). Mitosis detection in intestinal crypt images with Hough forest and Conditional Random Fields. In Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 287-295). Athens, GRC: Springer Verlag.

MLA:

Bortsova, Gerda, et al. "Mitosis detection in intestinal crypt images with Hough forest and Conditional Random Fields." Proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, GRC Ed. Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang, Springer Verlag, 2016. 287-295.

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