Starting the GUI

The quickest way to start is to open the GUI from a command line terminal. You might need to open an anaconda prompt if you did not add anaconda to the path:

python -m cellpose

The first time cellpose runs it downloads the latest available trained model weights from the website.

You can drag and drop images (.tif, .png, .jpg, .gif) into the GUI and run Cellpose, and/or manually segment them. When the GUI is processing, you will see the progress bar fill up and during this time you cannot click on anything in the GUI. For more information about what the GUI is doing you can look at the terminal/prompt you opened the GUI with. For example data, see cellpose website. For best accuracy and runtime performance, resize images so cells are less than 100 pixels across.

For multi-channel, multi-Z tiff’s, the expected format is Z x channels x Ly x Lx.

For multi-Z 3D data, please use the 3D version of the GUI:

python -m cellpose --Zstack


The output file with the masks is in the same folder as the loaded image with _seg.npy appended. The GUI automatically saves after you draw an ROI but NOT after running a model for segmentation and NOT after 3D mask drawing (too slow). Save in the file menu or with Ctrl+S.


Since the output file is in the same folder as the loaded image with _seg.npy appended, make sure you have WRITE access in the folder, otherwise the file will not save.

Using the GUI

The GUI serves two main functions:

  1. Running the segmentation algorithm.

  2. Manually labelling data.

  3. (NEW) Fine-tuning a pretrained cellpose model on your own data.

Main GUI mouse controls (works in all views):

  • Pan = left-click + drag

  • Zoom = scroll wheel (or +/= and - buttons)

  • Full view = double left-click

  • Select mask = left-click on mask

  • Delete mask = Ctrl (or Command on Mac) + left-click

  • Merge masks = Alt + left-click (will merge last two)

  • Start draw mask = right-click

  • End draw mask = right-click, or return to circle at beginning

Drawing masks

Masks are started with right-click, then hover your mouse (do not hold it down), and return it to the red circle to complete the mask. The mask should now be completed.

Overlaps in masks are NOT allowed. If you draw a mask on top of another mask, it is cropped so that it doesn’t overlap with the old mask. Masks in 2D should be single strokes (if single_stroke is checked).

If you want to draw masks in 3D, then you can turn single_stroke option off and draw a stroke on each plane with the cell and then press ENTER. You can also draw multiple strokes on the same plane for complex cell shapes, but do not do this in 2D if you plan to train a cellpose model (the cell flows will not work correctly).


3D labelling will fill in unlabelled z-planes so that you do not have to densely label, for example you can skip some planes, and the cell will be interpolated between planes.

After each mask is drawn in 2D, it is saved to the _seg.npy. If this is slow (for large images), this “autosave” option can be turned off in the “File” menu (“Disable autosave _seg.npy file”). In 3D, the mask is never auto-saved, instead save masks by clicking CTRL+S, or “Save” in the “File” menu.

Bulk Mask Deletion

Clicking the ‘delete multiple’ button will allow you to select and delete multiple masks at once. Masks can be deselected by clicking on them again. Once you have selected all the masks you want to delete, click the ‘done’ button to delete them.

Alternatively, you can create a rectangular region to delete a regions of masks by clicking the ‘delete multiple’ button, and then moving and/or resizing the region to select the masks you want to delete. Once you have selected the masks you want to delete, click the ‘done’ button to delete them.

At any point in the process, you can click the ‘cancel’ button to cancel the bulk deletion.

Segmentation options

SIZE: you can manually enter the approximate diameter for your cells, or press “calibrate” to let the model estimate it. The size is represented by a disk at the bottom of the view window (can turn this disk off by unchecking “scale disk on”).

use GPU: if you have installed the cuda version of mxnet, then you can activate this, but it won’t give huge speedups when running single images in the GUI.

MODEL: there is a cytoplasm model and a nuclei model, choose what you want to segment

CHAN TO SEG: this is the channel in which the cytoplasm or nuclei exist

CHAN2 (OPT): if cytoplasm model is chosen, then choose the nuclear channel for this option

Training your own cellpose model

Check out this video to learn the process.

  1. Drag and drop an image from a folder of images with a similar style (like similar cell types).

  2. Run the built-in models on one of the images using the “model zoo” and find the one that works best for your data. Make sure that if you have a nuclear channel you have selected it for CHAN2.

  3. Fix the labelling by drawing new ROIs (right-click) and deleting incorrect ones (CTRL+click). The GUI autosaves any manual changes (but does not autosave after running the model, for that click CTRL+S). The segmentation is saved in a _seg.npy file.

  4. Go to the “Models” menu in the File bar at the top and click “Train new model…” or use shortcut CTRL+T.

  5. Choose the pretrained model to start the training from (the model you used in #2), and type in the model name that you want to use. The other parameters should work well in general for most data types. Then click OK.

  6. The model will train (much faster if you have a GPU) and then auto-run on the next image in the folder. Next you can repeat #3-#5 as many times as is necessary.

  7. The trained model is available to use in the future in the GUI in the “custom model” section and is saved in your image folder.

If you have 3D data, please save random XY, YZ and XZ slices through your 3D data, ideally sufficiently spaced from each other so the information each slice has is distinct. Then put these slices into a folder and start the human-in-the-loop training. You can then use the new custom model on new 3D data.


You can only start training with one of the built-in Cellpose models or from scratch. When you start training from a built-in model or from scratch each time, then you are training the network on all the previously labelled images in the folder and weighting them equally in your training set.

If you restart from a previous retraining, you are biasing the network towards the earlier images it has already been trained on. Conversely, if you have created a custom model with different images, and you retrain that model, then you are downweighting the images that you have already trained on and excluded from your new training set. Therefore, we recommend having all images that you want to be trained for the same model in the same folder so they are all used.

See the Models doc for info on the new model zoo and suggestion mode.

Contributing training data

We are very excited about receiving community contributions to the training data and re-training the cytoplasm model to make it better. Please follow these guidelines:

  1. Run cellpose on your data to see how well it does. Try varying the diameter, which can change results a little.

  2. If there are relatively few mistakes, it won’t help much to contribute labelled data.

  3. If there are consistent mistakes, your data is likely very different from anything in the training set, and you should expect major improvements from contributing even just a few manually segmented images.

  4. For images that you contribute, the cells should be at least 10 pixels in diameter, and there should be at least several dozens of cells per image, ideally ~100. If your images are too small, consider combining multiple images into a single big one and then manually segmenting that. If they are too big, consider splitting them into smaller crops.

  5. For the manual segmentation, please try to outline the boundaries of the cell, so that everything (membrane, cytoplasm, nucleus) is inside the boundaries. Do not just outline the cytoplasm and exclude the membrane, because that would be inconsistent with our own labelling and we wouldn’t be able to use that.

  6. Do not use the results of the algorithm in any way to do contributed manual segmentations. This can reinforce a vicious circle of mistakes, and compromise the dataset for further algorithm development.

If you are having problems with the nucleus model, please open an issue before contributing data. Nucleus images are generally much less diverse, and we think the current training dataset already covers a very large set of modalities. Additionally, you can run a non-nuclear model on nuclear data such as cyto.

Keyboard shortcuts

Keyboard shortcuts




=/+ // -

zoom in // zoom out


undo previously drawn mask/stroke


clear all masks


load image (can alternatively drag and drop image)


SAVE MASKS IN IMAGE to _seg.npy file


start model training using _seg.npy files


load _seg.npy file (note: it will load automatically with image if it exists)


load masks file (must be same size as image with 0 for NO mask, and 1,2,3… for masks)


save masks as PNG


save ROIs to native ImageJ ROI format


save flows to image file


cycle through images in current directory


change color (RGB/gray/red/green/blue)

R / G / B

press to toggle RGB and Red or Green or Blue


change to flows and cell prob views (if segmentation computed)


turn masks ON or OFF


toggle outlines ON or OFF

, / .

increase / decrease brush size for drawing