Dong, Bo, Shao, Ling, da Costa, Marc, Bandmann, Oliver and Frangi, Alejandro F. (2015) Deep Learning for Automatic Cell Detection in Wide-Field Microscopy Zebrafish Images. In: ISBI '15: International Symposium on Biomedical Imaging, 16th - 19th April 2015, New York, US.
Full text not available from this repository. (Request a copy)Abstract
The zebrafish has become a popular experimental model organism for biomedical research. In this paper, a unique framework is proposed for automatically detecting Tyrosine Hydroxylase-containing (TH-labeled) cells in larval zebrafish brain z-stack images recorded through the wide-field microscope. In this framework, a supervised max-pooling Convolutional Neural Network (CNN) is trained to detect cell pixels in regions that are preselected by a Support Vector Machine (SVM) classifier. The results show that the proposed deep-learned method outperforms hand-crafted techniques and demonstrate its potential for automatic cell detection in wide-field microscopy z-stack zebrafish images.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Machine learning, Microscopy - Light, Single cell & molecule detection |
| Subjects: | G400 Computer Science |
| Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
| Related URLs: | |
| Depositing User: | Paul Burns |
| Date Deposited: | 16 Jun 2015 08:26 |
| Last Modified: | 13 Oct 2019 00:20 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/22922 |
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