– If using ITCN in published work, cite: “Image-based Tool for Counting Nuclei (ITCN)” – available via ImageJ.net, and reference the ImageJ software (Schneider et al., 2012, Nat Methods).
Abstract Quantifying cell numbers from microscopy images is a cornerstone of biological assays, yet manual counting remains tedious and biased. The ITCN (Image-based Tool for Counting Nuclei) plugin for ImageJ/Fiji offers an automated, tunable, and accessible solution. This article provides a technical deep dive into its algorithm, practical workflow, performance benchmarks, and limitations relative to modern deep-learning alternatives. 1. Introduction For decades, biologists have faced a fundamental bottleneck: converting visual information into discrete numerical data. Whether quantifying viral infectivity, assessing neurogenesis, or measuring tumor infiltration, counting DAPI-, Hoechst-, or Nissl-stained nuclei is essential. itcn imagej plugin
Every bioimage analyst should have ITCN in their toolkit. Use it as the default automated counter; switch to alternatives only when validation reveals systematic bias. Acknowledgments – Original ITCN plugin authored by Dr. Jeffrey E. Boyd and the Center for Bio-Image Informatics, UC Santa Barbara. – If using ITCN in published work, cite:
// Simple macro for batch counting dir = getDirectory("Choose Source Directory"); list = getFileList(dir); for (i=0; i<list.length; i++) open(dir+list[i]); run("ITCN", "width=15 min=10 threshold=20"); saveAs("Results", dir+list[i]+"_counts.csv"); close(); This article provides a technical deep dive into