Title :
Automated recognition of mitotic patterns in fluorescence microscopy images of human cells
Author :
Harder, N. ; Neumann, B. ; Held, M. ; Liebel, U. ; Erfle, H. ; Ellenberg, J. ; Eils, R. ; Rohr, K.
Author_Institution :
Dept. Bioinformatics & Functional Genomics, Heidelberg Univ.
Abstract :
High-throughput screens of the gene function provide rapidly increasing amounts of data. In particular, the analysis of image data acquired in genome-wide cell phenotype screens constitutes a substantial bottleneck in the evaluation process and motivates the development of automated image analysis tools for large-scale experiments. Here we introduce a computational scheme to process multi-cell images as they are produced in high-throughput screens. We describe an approach to automatically segment and classify cell nuclei into different mitotic phenotypes. This enables automated identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves a high classification accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments
Keywords :
biomedical optical imaging; cellular biophysics; fluorescence; genetics; image segmentation; medical image processing; optical microscopy; pattern recognition; automated image analysis tools; automated mitotic pattern recognition; cell nuclei classification; cell nuclei segmentation; fluorescence microscopy images; gene function; genome-wide cell phenotype screens; high-throughput screens; human cells; multicell image processing; Bioinformatics; Data analysis; Fluorescence; Genomics; Humans; Image analysis; Image recognition; Large-scale systems; Microscopy; Pattern recognition;
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-9576-X
DOI :
10.1109/ISBI.2006.1625093