# How to count cells in ImageJ (automatic and manual)

> Count cells in ImageJ: automatic (threshold, Watershed, Analyze Particles) or manual (Cell Counter, Multi-point), plus cells per mm² and % positive.

Published 2026-11-06 by LabCanvas team. https://www.labcanvas.com/blog/count-cells-imagej

![ImageJ 1.54f counting the synthetic nuclei sample: 70 nuclei outlined in yellow and numbered after an Otsu threshold, Fill Holes and Watershed, the Analyze Particles dialog with a size of 7 to Infinity µm² and Summarize ticked, and the Summary table with a count of 70](https://www.labcanvas.com/blog/imagej/count-cells-imagej-cover.webp)

To count cells automatically in ImageJ, convert the image to 8-bit, set a threshold with **Image › Adjust › Threshold…**, clean up the mask with **Process › Binary** (**Convert to Mask**, **Fill Holes**, **Watershed**), then run **Analyze › Analyze Particles…** with a size and circularity range and **Summarize** ticked: the Summary window gives the count. To count by hand, use **Plugins › Analyze › Cell Counter** in Fiji, or the counters of the Multi-point tool in plain ImageJ 1.x.

Automatic counting works best on stained nuclei (DAPI, Hoechst) and other fluorescent cells on a dark background. Unstained cells in brightfield or phase contrast are much harder, and this guide says what to do instead.

## Which method fits your image

| Your image | The route that works |
|---|---|
| Fluorescent nuclei or cells, mostly apart | Automatic: threshold, watershed, Analyze Particles |
| Crowded, touching nuclei | Automatic with watershed; for dense tissue, StarDist or Cellpose |
| Small bright cells or spots where only the number matters | Find Maxima |
| Unstained cells in brightfield or phase contrast | A trained pixel classifier, a deep-learning tool, or counting by hand |
| A few images, or cells you must judge by eye (shape, two markers) | Manual: Cell Counter or the Multi-point tool |
| Cells in a hemocytometer, for cells per mL | Count the cells in each large square, then use the [hemocytometer calculator](https://www.labcanvas.com/tools/cell-count-calculator) |
| Bacterial or yeast colonies on a plate | A colony counter: see [colony counting with ImageJ](https://www.labcanvas.com/blog/colony-counter-online) |

## Automatic counting in ImageJ (or Fiji)

These commands are part of ImageJ 1.x, so they are the same in plain ImageJ and in Fiji.

1. **Set the scale** with **Analyze › Set Scale…** if the file does not carry it, so that sizes come out in µm²; see [how to set the scale in ImageJ](https://www.labcanvas.com/blog/imagej-scale-bar-set-scale).
2. **Keep one channel**: for a multi-channel image, use **Image › Color › Split Channels** and keep the nuclear channel. Then **Image › Type › 8-bit**. That is fine for counting; measure intensities on the original file.
3. **Smooth and flatten, if needed**: **Process › Filters › Gaussian Blur…** with a sigma of 1 or 2 pixels evens out noise, and **Process › Subtract Background…** removes a bright corner or a gradient.
4. **Threshold**: open **Image › Adjust › Threshold…**, pick a method (Default, Otsu, Li, Triangle and others), tick **Dark background** for fluorescence, and check that the red overlay covers every nucleus and nothing else. Click **Apply**. The methods are compared side by side in [ImageJ threshold methods, with pictures](https://www.labcanvas.com/blog/imagej-threshold-methods).
5. **Clean up the mask**: **Process › Binary › Convert to Mask** (if Apply did not already make one), then **Process › Binary › Fill Holes** and **Process › Binary › Watershed**, which cuts touching round objects apart with one-pixel lines.
6. **Count**: run **Analyze › Analyze Particles…** with the settings below, then read **Count** in the Summary window, which also gives Total Area, Average Size and %Area.

| Analyze Particles setting | What to choose |
|---|---|
| Size (µm² once the scale is set) | A lower limit that drops debris, for example a fifth of a typical nucleus's area, up to Infinity |
| Circularity | 0.00–1.00 keeps everything; raise the lower limit to drop long debris and scratches |
| Show | Outlines or Overlay, so you can check what was counted |
| Display results, Summarize | Ticked: one row per cell, and the count |
| Exclude on edges | Ticked for measurements per cell; for density, see below |
| Include holes | Ticked, so a nucleus with a dim centre is measured whole, not as a ring |
| Add to Manager | Ticked if you want to measure each cell in another channel afterwards |

Before you trust the number, lay the outlines over the image and look: a merged pair, a split cell or a counted speck is easy to see there and invisible in the Summary. Every option in the dialog is explained in [Analyze Particles, explained](https://www.labcanvas.com/blog/analyze-particles-imagej).

**Only the number?** For small, round, bright cells, **Process › Find Maxima…** counts one point per peak without making a mask: set **Prominence >** until each cell gets one point, tick **Exclude edge maxima** if you need to, and choose **Output type: Count**.

![ImageJ Threshold window with nuclei shown in red](https://www.labcanvas.com/blog/imagej/ij-threshold.webp "ImageJ: Image › Adjust › Threshold on the nuclei sample with Dark background ticked; the selected pixels are red. The method is picked from the list on the left Default here; Otsu, Li, Triangle and the others are in the same list.")

![ImageJ Analyze Particles dialog over thresholded nuclei](https://www.labcanvas.com/blog/imagej/ij-analyze-particles.webp "ImageJ: Analyze › Analyze Particles, with the size range in calibrated units µm², circularity, what to show, and Display results, Summarize, Exclude on edges and Include holes.")

![ImageJ Summary table counting 70 nuclei](https://www.labcanvas.com/blog/imagej/ij-count-result.webp "ImageJ on the nuclei sample: Otsu threshold, Convert to Mask, Fill Holes, Watershed, then Analyze Particles from 7 µm². The Summary counts 70 nuclei, average size 40.394 µm², 10.2% of the area.")

## Manual counting: Cell Counter (Fiji) or the Multi-point tool (ImageJ 1.x)

Counting by hand is still the reference when cells touch, when you classify them by eye, or when you check an automatic count.

### In Fiji: Plugins › Analyze › Cell Counter

1. Open the image, then **Plugins › Analyze › Cell Counter › Cell Counter**.
2. Click **Initialize**.
3. Select a counter type (Type 1, Type 2 and so on; you can add more) and click each cell. A numbered marker of that type appears on each one.
4. Click **Results** for the counts of every type (for a stack, per slice and in total).

Plain ImageJ 1.x does not include the Cell Counter plugin.

### In plain ImageJ 1.x: the Multi-point tool and its counters

1. Right-click the point tool in the toolbar and choose **Multi-point Tool**.
2. Double-click the tool (or use **Edit › Options › Point Tool…**) and choose a **Counter**: 0 for the first class of cell, 1 for the second, and so on. The dialog shows the count for the current counter.
3. Click each cell. Alt-click (or Ctrl-click) a point to remove it.
4. **Analyze › Measure** lists every point with its counter; Alt+Y shows the counts per counter in a table.

The ImageJ wiki notes that the Multi-point tool now does most of what Cell Counter does, so you do not need Fiji just to count by hand.

## Cells per mm² and percent positive

**Density.** Cells per mm² = the count ÷ the area of the field in mm². The field area is the width × the height in pixels × the pixel size squared. The built-in nuclei sample used below is 512 × 512 pixels at 0.325 µm per pixel: 166.4 µm square, or 0.0277 mm². Its 70 nuclei are about 2,530 nuclei per mm².

For density, do not simply exclude the cells cut by the edge, or you undercount: either count them all, or use a counting frame (keep the cells touching two chosen edges, drop those touching the other two). For cells per mL from a hemocytometer, the mean count per large square × 10,000 × the dilution gives cells/mL; the [hemocytometer calculator](https://www.labcanvas.com/tools/cell-count-calculator) does the sums, with viability and seeding volumes.

**Percent positive** \(= \text{positive cells} \div \text{all cells} \times 100\), for Ki-67, EdU, TUNEL or a reporter. In ImageJ: count all nuclei in the nuclear channel with Analyze Particles and **Add to Manager** ticked, select the marker channel, and click **Measure** in the ROI Manager (with **Mean gray value** ticked in **Analyze › Set Measurements…**). Each nucleus gets its mean marker intensity; count those above a cut-off you set on a negative control. By hand, use two counters, positive and negative.

## When a threshold cannot separate the cells

Uneven staining, textured cells and brightfield or phase-contrast images defeat a single threshold. In Fiji, **Plugins › Segmentation › Trainable Weka Segmentation** lets you paint a few strokes on each class (cell and background to start); a random forest then classifies every other pixel, and you paint more where it is wrong. **Create result** gives a class image you threshold and count with Analyze Particles as above (Arganda-Carreras et al. 2017).

Be realistic with unstained cells: a classifier helps when cells differ from the background in texture or colour, but cells that touch with no visible boundary still merge. For those, deep-learning segmentation such as Cellpose (Stringer et al. 2021) or StarDist, which run in Python or as Fiji plugins, or counting by hand, is the honest answer.

## Pitfalls

- **Merged and split cells.** Watershed separates touching round nuclei, but it also cuts long or bent cells in two. Compare the outlines with the image, and leave watershed off for elongated cells; [watershed for touching particles](https://www.labcanvas.com/blog/watershed-touching-particles) shows both cases.
- **Debris and noise counted as cells.** Set a minimum size (a fifth of a typical nucleus works for most nuclear stains), blur slightly before thresholding, and use circularity to drop scratches.
- **Uneven illumination.** One threshold for a field that is brighter on one side picks up background there and misses dim cells elsewhere. Subtract the background first (**Process › Subtract Background…**), or train a classifier.
- **A different threshold for every image.** Choose the method once, on a few representative images, and use the same settings for every image in the experiment; adjusting each image towards the answer you expect biases the result.
- **An inverted mask.** One huge particle, or a count of holes, means the background became the object: check **Process › Binary › Options › Black background**, or invert the mask.
- **Nuclei are not always cells.** Multinucleated cells, dividing cells and apoptotic fragments break the one-nucleus-one-cell rule; say what you counted.
- **Never checked against a human.** Count three to five fields by hand and compare them with the automatic count before you run hundreds of images.

## Reporting it

A methods sentence to copy, with the parts in brackets filled in:

> Nuclei were counted in [Fiji (ImageJ 1.54)] on [DAPI] images ([n] fields per well, [20×] objective, [0.325] µm per pixel): [Otsu] threshold, Fill Holes and Watershed, then Analyze Particles with a size range of [7 µm²] to infinity and circularity [0.30–1.00]. Counts were converted to nuclei per mm² using the field area ([0.0277] mm²). Nuclei were scored [Ki-67]-positive when their mean intensity exceeded [a cut-off set on the secondary-antibody-only control].

Cite ImageJ (Schneider et al. 2012) or Fiji (Schindelin et al. 2012), and name the threshold method; Otsu's method is Otsu (1979).

To show the result, plot cells per mm² or percent positive for each condition with every biological replicate (well, animal or independent experiment) as a point, not every field, and the mean ± SD. For the figure, **Image › Overlay › Flatten** burns the outlines into an RGB copy, and **Analyze › Tools › Scale Bar…** adds the scale bar.

## An easier way: count cells in LabCanvas

The same count runs in the browser in [LabCanvas Image Analysis](https://www.labcanvas.com/analysis.html), with nothing to install and no plugins.

1. Open your TIFF, or click the nuclei sample on the start screen.
2. Go to the **Count** tab.
3. Click **Find nuclei** for the one-click route (Otsu threshold, holes filled, touching nuclei split, specks under a fifth of the typical nucleus left out), or set the threshold, **Fill holes**, **Separate touching objects (watershed)** and the size range yourself in **Count particles** and click **Count**.
4. Read the Summary row (Count, Total Area, Average Size, %Area, Mean); for clicking by hand, the **Cell counter** in the same tab counts in up to 20 named categories with their percentages.

:::grid
![Counting nuclei in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/nuclei.webp "Counting nuclei: 70 nuclei counted Otsu threshold, watershed to split touching ones: mean area 40.47 µm², 10.2% of the field.")
![Pixel classifier in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/classifier.webp "The pixel classifier on the built-in nuclei sample: trained on 1,018 painted pixels, 70 objects counted, the Object class covering 12.3% of the image.")
![Spot detection in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/spots.webp "Spot detection: 150 spots found in the green channel, with the prominence 132.5 set automatically from the image's noise.")
:::

On the synthetic nuclei sample (70 nuclei by design), ImageJ counts 70 with Otsu, Fill Holes and Watershed, and **Find nuclei** in LabCanvas also counts all 70.

Why it is a good choice for cell counts:

- **Nothing to install**: it runs in the browser on any computer or tablet, so you can count at the bench.
- **ImageJ's own names**: Count particles is Analyze Particles, with the same sixteen threshold methods, fill holes, watershed, size and circularity.
- **A pixel classifier built in**, for cells a threshold cannot separate, instead of installing Trainable Weka Segmentation.
- **An image check first**: it flags clipped pixels, JPEG compression and uneven light (above 1.3×) before you count.
- **Straight into Graphs**: **Send to Graphs** plots the counts per condition, and every table carries a **Methods** sentence to copy. Counting, **Copy** and **Send to Graphs** are free with a LabCanvas account; downloading CSV files is part of LabCanvas Pro.

It does not run deep-learning segmentation (StarDist, Cellpose), 3D counting or macros, and files in a microscope maker's own format (.czi, .nd2, .lif) need exporting as TIFF first; for those, Fiji is the better tool. Every measurement is validated in [more than 4,000 automated tests](https://www.labcanvas.com/image-analysis-validation). [Open Image Analysis](https://www.labcanvas.com/analysis.html). ImageJ and Fiji are independent open-source projects; LabCanvas is not affiliated with them.

## References

- Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS (2017). Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. *Bioinformatics* 33, 2424–2426. [doi:10.1093/bioinformatics/btx180](https://doi.org/10.1093/bioinformatics/btx180)
- Otsu N (1979). A threshold selection method from gray-level histograms. *IEEE Transactions on Systems, Man, and Cybernetics* 9, 62–66. [doi:10.1109/TSMC.1979.4310076](https://doi.org/10.1109/TSMC.1979.4310076)
- Schindelin J, Arganda-Carreras I, Frise E, et al. (2012). Fiji: an open-source platform for biological-image analysis. *Nature Methods* 9, 676–682. [doi:10.1038/nmeth.2019](https://doi.org/10.1038/nmeth.2019)
- Schneider CA, Rasband WS, Eliceiri KW (2012). NIH Image to ImageJ: 25 years of image analysis. *Nature Methods* 9, 671–675. [doi:10.1038/nmeth.2089](https://doi.org/10.1038/nmeth.2089)
- Stringer C, Wang T, Michaelos M, Pachitariu M (2021). Cellpose: a generalist algorithm for cellular segmentation. *Nature Methods* 18, 100–106. [doi:10.1038/s41592-020-01018-x](https://doi.org/10.1038/s41592-020-01018-x)

## Frequently asked questions

### How do I count cells automatically in ImageJ?

Convert the image to 8-bit (Image › Type › 8-bit), set a threshold with Image › Adjust › Threshold so that the cells are red and the background is not, and click Apply. Then run Process › Binary › Fill Holes and Process › Binary › Watershed to split touching cells, and Analyze › Analyze Particles with a size range, Show: Outlines and Summarize ticked. The Summary window gives the count.

### Where is the Cell Counter plugin in ImageJ?

Cell Counter comes with Fiji, under Plugins › Analyze › Cell Counter › Cell Counter: click Initialize, pick a counter type and click each cell, then Results for the counts. Plain ImageJ 1.x does not include it; there, use the Multi-point tool, whose options (double-click the tool) let you choose a counter for each class of cell.

### How do I separate touching cells before counting?

Make a binary mask (threshold, then Process › Binary › Convert to Mask) and run Process › Binary › Watershed, which cuts touching round objects apart with one-pixel lines. It works well on nuclei, but it can cut long or irregular cells in two, so check the outlines. For crowded or irregular cells, deep-learning tools such as StarDist or Cellpose separate them better.

### Why does Analyze Particles count only one huge particle?

The mask is the wrong way round: the background has become the object. Check Process › Binary › Options › Black background, make sure the cells are the thresholded (red) pixels before you apply the threshold, or invert the mask with Edit › Invert, and run Analyze Particles again.

### Can ImageJ count cells in brightfield or phase-contrast images?

Not reliably with a plain threshold, because unstained cells differ from the background more in texture and halos than in brightness. A trained pixel classifier (Trainable Weka Segmentation in Fiji), a deep-learning tool such as Cellpose, or counting by hand works better. If you can, stain the nuclei and count those instead.

### How do I convert a cell count to cells per mm²?

Divide the count by the area of the field in mm². The field area is the width times the height in pixels, times the pixel size squared: 512 × 512 pixels at 0.325 µm per pixel is 166.4 µm × 166.4 µm, or 0.0277 mm². For cells per mL from a hemocytometer, use the hemocytometer formula instead.

### Is there an easier way to count cells without installing ImageJ?

Yes: LabCanvas Image Analysis runs in the browser and has Find nuclei, which thresholds, fills holes, splits touching nuclei and counts in one click; see the last section of this guide or open it at labcanvas.com/analysis.html.

## Related links

- [Analyze Particles in ImageJ, explained](https://www.labcanvas.com/blog/analyze-particles-imagej): Every option in the dialog
- [ImageJ threshold methods compared](https://www.labcanvas.com/blog/imagej-threshold-methods): The same image under all 16 methods
- [Colony counting with ImageJ](https://www.labcanvas.com/blog/colony-counter-online): Count colonies from a plate photo
- [Hemocytometer cell count calculator](https://www.labcanvas.com/tools/cell-count-calculator): Cells/mL and viability from your counts
- [Image Analysis: the complete guide](https://www.labcanvas.com/blog/image-analysis-complete-guide): Every tool and setting in the app, with examples
- [How Image Analysis is validated](https://www.labcanvas.com/image-analysis-validation): More than 4,000 checks against independent software