Chen, S, Gehrer, S, Kaliman, S et al. (7 more authors) (2019) Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration. In: Handels, H, Deserno, TM, Maier, A, Maier-Hein, KH, Palm, C and Tolxdorff, T, (eds.) Informatik aktuell. BVM 2019: Bildverarbeitung für die Medizin 2019, 17-19 Mar 2019, Lübeck, Germany. Springer Vieweg , pp. 116-121. ISBN 978-3-658-25325-7
Abstract
In the field of biophysics, deformation of in-vitro model tissues is an experimental technique to explore the response of tissue to a mechanical stimulus. However, automated registration before and after deformation is an ongoing obstacle for measuring the tissue response on the cellular level. Here, we propose to use an iterative point cloud registration (IPCR) method, for this problem. We apply the registration method on point clouds representing the cellular centers of mass, which are evaluated with aWatershed based segmentation of phase-contrast images of living tissue, acquired before and after deformation. Preliminary evaluation of this method on three data sets shows high accuracy, with 82% - 92% correctly registered cells, which outperforms coherent point drift (CPD). Hence, we propose the application of the IPCR method on the problem of cell correspondence analysis.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019. This is a post-peer-review, pre-copyedit version of an article published in Informatik aktuell. The final authenticated version is available online at: https://doi.org/10.1007/978-3-658-25326-4_26. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2019 10:14 |
Last Modified: | 07 Feb 2020 01:39 |
Status: | Published |
Publisher: | Springer Vieweg |
Identification Number: | 10.1007/978-3-658-25326-4_26 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149279 |