Deep learning for automatic cell detection in wide-field microscopy zebrafish images
| Autor: | Marc Da Costa, Ling Shao, Oliver Bandmann, Bo Dong, Alejandro F. Frangi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Microscope
biology Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION biology.organism_classification Convolutional neural network law.invention Support vector machine law Histogram Microscopy Computer vision Artificial intelligence business Zebrafish |
| Zdroj: | ISBI |
| DOI: | 10.1109/isbi.2015.7163986 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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