Deep Learning for Automatic Cell Detection in Wide-Field Microscopy Zebrafish Images

Bo Dong, Ling Shao, Marc da Costa, Oliver Bandmann, Alejandro F. Frangi

Research output: Contribution to conferencePaper

36 Citations (Scopus)

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.
Original languageEnglish
Publication statusPublished - Apr 2015
EventISBI '15: International Symposium on Biomedical Imaging - New York, US
Duration: 1 Apr 2015 → …

Conference

ConferenceISBI '15: International Symposium on Biomedical Imaging
Period1/04/15 → …

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