Anzeige
Erschienen in:
2019 | OriginalPaper | Buchkapitel
Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration
verfasst von : Shuqing Chen, Simone Gehrer, Sara Kaliman, Nishant Ravikumar, Abdurrahman Becit, Maryam Aliee, Diana Dudziak, Rudolf Merkel, Ana-Sunćana Smith, Andreas Maier
Erschienen in: Bildverarbeitung für die Medizin 2019
Verlag: Springer Fachmedien Wiesbaden
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.