An automated method for cell detection in Zebrafish

Tianming Liu, Gang Li, Jingxin Nie, Ashley Tarokh, Xiaobo Zhou, Lei Guo, Jarema Malicki, Weiming Xia, Stephen T.C. Wong

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

Original languageEnglish (US)
Pages (from-to)5-21
Number of pages17
Issue number1
StatePublished - Mar 2008


  • Alzheimer's disease
  • Image processing
  • Modeling
  • Neuronal cell detection
  • Retina development
  • Zebrafish

ASJC Scopus subject areas

  • Neuroscience(all)
  • Information Systems
  • Software


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