Flexible synapse detection in fluorescence micrographs by modeling human expert grading
A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffuse signals. To automate synapse detection in such images, one has to simulate aspects of human pattern recognition skills...
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| creator | Herold, J. Friedenberger, M. Bode, M. Rajpoot, N. Schubert, W. Nattkemper, T.W. |
| description | A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffuse signals. To automate synapse detection in such images, one has to simulate aspects of human pattern recognition skills to account for low signal-to-noise-ratios. We propose a machine learning based method that allows a direct integration of the experts' visual expertise who tag a low number of referential synapses according to their degree of synapse likeness. The sensitivity and positive predictive values show that by using graded likeness information in our learning algorithm we can provide an intuitively tunable tool for neural tissue slide evaluation. |
| doi_str_mv | 10.1109/ISBI.2008.4541254 |
| format | Conference Proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1945-7928 |
| ispartof | 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, p.1347-1350 |
| issn | 1945-7928 1945-8452 |
| language | eng |
| recordid | cdi_ieee_primary_4541254 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Fluorescence Fluorescence microscopy High-resolution imaging Humans Image analysis Image edge detection Image resolution Image texture analysis Medical signal detection Microscopy neural tissue Object detection supervised machine learning synapses |
| title | Flexible synapse detection in fluorescence micrographs by modeling human expert grading |
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