The cellpose is a generalist algorithm for cellular segmentation with human-in-the-loop capabilities.
cellpose -h
cellpose-gui
vglrun cellpose-gui
Allocate an interactive session and run the program.
Sample session (user input in bold):
[user@biowulf]$ sinteractive --gres=gpu:p100:1 --mem=8g
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job
[user@cn3144 ~]$ module load cellpose
[user@cn3144 ~]$ mkdir /data/$USER/cellpose_test/
[user@cn3144 ~]$ cd /data/$USER/cellpose_test/
[user@cn3144 ~]$ cellpose -h
/opt/conda/lib/python3.8/site-packages/pyqtgraph/colors/palette.py:1: RuntimeWarning: PyQtGraph supports Qt version >= 5.15, but 5.12.9 detected.
from ..Qt import QtGui
usage: cellpose [-h] [--use_gpu] [--gpu_device GPU_DEVICE] [--check_mkl] [--dir DIR] [--image_path IMAGE_PATH] [--look_one_level_down]
[--img_filter IMG_FILTER] [--channel_axis CHANNEL_AXIS] [--z_axis Z_AXIS] [--chan CHAN] [--chan2 CHAN2] [--invert] [--all_channels]
[--pretrained_model PRETRAINED_MODEL] [--add_model ADD_MODEL] [--unet] [--nclasses NCLASSES] [--no_resample] [--net_avg] [--no_interp]
[--no_norm] [--do_3D] [--diameter DIAMETER] [--stitch_threshold STITCH_THRESHOLD] [--fast_mode] [--flow_threshold FLOW_THRESHOLD]
[--cellprob_threshold CELLPROB_THRESHOLD] [--anisotropy ANISOTROPY] [--exclude_on_edges] [--save_png] [--save_tif] [--no_npy]
[--savedir SAVEDIR] [--dir_above] [--in_folders] [--save_flows] [--save_outlines] [--save_ncolor] [--save_txt] [--train] [--train_size]
[--test_dir TEST_DIR] [--mask_filter MASK_FILTER] [--diam_mean DIAM_MEAN] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY]
[--n_epochs N_EPOCHS] [--batch_size BATCH_SIZE] [--min_train_masks MIN_TRAIN_MASKS] [--residual_on RESIDUAL_ON] [--style_on STYLE_ON]
[--concatenation CONCATENATION] [--save_every SAVE_EVERY] [--save_each] [--verbose]
cellpose parameters
optional arguments:
-h, --help show this help message and exit
--verbose show information about running and settings and save to log
hardware arguments:
--use_gpu use gpu if torch with cuda installed
--gpu_device GPU_DEVICE
which gpu device to use, use an integer for torch, or mps for M1
--check_mkl check if mkl working
input image arguments:
--dir DIR folder containing data to run or train on.
--image_path IMAGE_PATH
if given and --dir not given, run on single image instead of folder (cannot train with this option)
--look_one_level_down
run processing on all subdirectories of current folder
--img_filter IMG_FILTER
end string for images to run on
--channel_axis CHANNEL_AXIS
axis of image which corresponds to image channels
--z_axis Z_AXIS axis of image which corresponds to Z dimension
--chan CHAN channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE. Default: 0
--chan2 CHAN2 nuclear channel (if cyto, optional); 0: NONE, 1: RED, 2: GREEN, 3: BLUE. Default: 0
--invert invert grayscale channel
--all_channels use all channels in image if using own model and images with special channels
model arguments:
--pretrained_model PRETRAINED_MODEL
model to use for running or starting training
--add_model ADD_MODEL
model path to copy model to hidden .cellpose folder for using in GUI/CLI
--unet run standard unet instead of cellpose flow output
--nclasses NCLASSES if running unet, choose 2 or 3; cellpose always uses 3
algorithm arguments:
--no_resample disable dynamics on full image (makes algorithm faster for images with large diameters)
--net_avg run 4 networks instead of 1 and average results
--no_interp do not interpolate when running dynamics (was default)
--no_norm do not normalize images (normalize=False)
--do_3D process images as 3D stacks of images (nplanes x nchan x Ly x Lx
--diameter DIAMETER cell diameter, if 0 will use the diameter of the training labels used in the model, or with built-in model will estimate diameter
for each image
--stitch_threshold STITCH_THRESHOLD
compute masks in 2D then stitch together masks with IoU>0.9 across planes
--fast_mode now equivalent to --no_resample; make code run faster by turning off resampling
--flow_threshold FLOW_THRESHOLD
flow error threshold, 0 turns off this optional QC step. Default: 0.4
--cellprob_threshold CELLPROB_THRESHOLD
cellprob threshold, default is 0, decrease to find more and larger masks
--anisotropy ANISOTROPY
anisotropy of volume in 3D
--exclude_on_edges discard masks which touch edges of image
output arguments:
--save_png save masks as png and outlines as text file for ImageJ
--save_tif save masks as tif and outlines as text file for ImageJ
--no_npy suppress saving of npy
--savedir SAVEDIR folder to which segmentation results will be saved (defaults to input image directory)
--dir_above save output folders adjacent to image folder instead of inside it (off by default)
--in_folders flag to save output in folders (off by default)
--save_flows whether or not to save RGB images of flows when masks are saved (disabled by default)
--save_outlines whether or not to save RGB outline images when masks are saved (disabled by default)
--save_ncolor whether or not to save minimal "n-color" masks (disabled by default
--save_txt flag to enable txt outlines for ImageJ (disabled by default)
training arguments:
--train train network using images in dir
--train_size train size network at end of training
--test_dir TEST_DIR folder containing test data (optional)
--mask_filter MASK_FILTER
end string for masks to run on. use "_seg.npy" for manual annotations from the GUI. Default: _masks
--diam_mean DIAM_MEAN
mean diameter to resize cells to during training -- if starting from pretrained models it cannot be changed from 30.0
--learning_rate LEARNING_RATE
learning rate. Default: 0.2
--weight_decay WEIGHT_DECAY
weight decay. Default: 1e-05
--n_epochs N_EPOCHS number of epochs. Default: 500
--batch_size BATCH_SIZE
batch size. Default: 8
--min_train_masks MIN_TRAIN_MASKS
minimum number of masks a training image must have to be used. Default: 5
--residual_on RESIDUAL_ON
use residual connections
--style_on STYLE_ON use style vector
--concatenation CONCATENATION
concatenate downsampled layers with upsampled layers (off by default which means they are added)
--save_every SAVE_EVERY
number of epochs to skip between saves. Default: 100
--save_each save the model under a different filename per --save_every epoch for later comparsion
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$
Open cellpose-gui inside of NoMachine:
[user@biowulf]$ sinteractive --gres=gpu:p100:1 --mem=8g salloc.exe: Pending job allocation 46116226 salloc.exe: job 46116226 queued and waiting for resources salloc.exe: job 46116226 has been allocated resources salloc.exe: Granted job allocation 46116226 salloc.exe: Waiting for resource configuration salloc.exe: Nodes cn3144 are ready for job [user@cn3144 ~]$ module load cellpose [user@cn3144 ~]$ cellpose-gui
Open cellpose-gui through visual partition svis desktop session:
[user@cn0655]$ module load virtualgl [user@cn0655]$ module load cellpose [user@cn0655]$ vglrun cellpose-gui
Create a batch input file (e.g. cellpose.sh). For example:
#!/bin/bash
set -e
module load cellpose
cellpose --train --dir /path/to/images/ --pretrained_model None --chan 3 --chan2 0 --use_gpu --verbose
Submit this job using the Slurm sbatch command.
sbatch --partition=gpu --gres=gpu:p100:1 --mem=8g cellpose.sh
Create a swarmfile (e.g. cellpose.swarm). For example:
cellpose --train --dir /path/to/images1/ --pretrained_model None --chan 3 --chan2 0 --use_gpu --verbose cellpose --train --dir /path/to/images2/ --pretrained_model None --chan 3 --chan2 0 --use_gpu --verbose
Submit this job using the swarm command.
swarm -f cellpose.swarm [-t #] [-g #] --partition=gpu --gres=gpu:p100:1 --module cellposewhere
| -g # | Number of Gigabytes of memory required for each process (1 line in the swarm command file) |
| -t # | Number of threads/CPUs required for each process (1 line in the swarm command file). |
| --module cellpose | Loads the cellpose module for each subjob in the swarm |