Plugin [updated] | Itcn Imagej

– 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).

// 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(); itcn imagej plugin

Use ITCN on the nuclear channel (e.g., DAPI) to generate a region-of-interest (ROI) set, then measure mean intensity in a cytoplasmic marker channel via Multi Measure . C. Adjusting for variable nucleus size If your sample has two distinct populations (e.g., microglia vs. neurons), run ITCN twice with different width values. Overlap suppression will require manual merging of results. 6. Limitations and Known Failure Modes | Problem | Manifestation | Workaround | |---------|---------------|-------------| | Intensity gradient across field | Fewer nuclei counted on dim side | Apply Process > Subtract Background (rolling ball radius = 2x nucleus width) before ITCN | | Highly clumped nuclei (e.g., liver sections) | Undercounting by 20–40% | Use Plugins > Segmentation > Watershed before ITCN, or switch to StarDist (deep learning) | | Non-spherical nuclei (e.g., smooth muscle) | Overcounts (splits elongated nuclei) | Use manual thresholding + Analyze Particles with circularity filter (0.6–1.0) | | Very low SNR | False positives from noise | Apply Process > Filters > Median (radius 2) pre-filtering | 7. ITCN vs. Modern Alternatives (2025 Perspective) | Tool | Strengths | Weaknesses | Best for | |------|-----------|------------|----------| | ITCN | No training, fast, interpretable | Fails on irregular shapes, intensity gradients | Routine, well-stained spherical nuclei | | StarDist (QuPath/ImageJ) | Handles any shape, excellent accuracy | Requires training data (~50–100 annotated images) | Complex tissues, variable morphology | | Cellpose | Outstanding on heterogeneous data | Heavy GPU requirements, overkill for simple assays | Unusual cell types, phase-contrast images | | Trainable Weka Segmentation | Good for texture-based separation | Slow, manual feature selection | Images with texture but poor contrast | – If using ITCN in published work, cite: