Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration
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.
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- 2.Mualla F, Schöll S, Sommerfeldt B, et al. Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm. Med Image Comput Comput Assist Interv. 2014; p. 377-384.Google Scholar
- 6.Gehrer S. Stress-strain relation in reconstituted tissue. Friedrich-Alexander-Universität Erlangen-Nürnberg; 2016.Google Scholar
- 9.Kaliman S, Jayachandran C, Rehfeldt F, et al. Limits of applicability of the voronoi tessellation determined by centers of cell nuclei to epithelium morphology. Front Physiol. 2016;7.Google Scholar
- 12.Muja M, Lowe DG. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP. 2009; p. 331-340.Google Scholar
- 13.Hansen N. The CMA evolution strategy: a comparing review. Towards New Evol Comput. 2006; p. 75-102.Google Scholar