multicellpose 0.9.9.11

MultiCellPose: segmentation and analysis toolkit for multimodal microscopy

MultiCellPose: Trainer + Analyzer

This repository provides a GUI workflow for labeling, remote inference, and training CellposeSAM-based multi-class segmentation models, plus an Analyzer app which allows running custom models and performing custom analysis plugins on detected objects.. It supports local or remote (SSH + gRPC) compute. Note: currently you will need to run the server yourself if you have a remote gpu-enable ssh server.

License note: MultiCellPose is distributed under BSD-3-Clause and includes upstream Cellpose components. Original Cellpose copyright remains in LICENSE (Copyright (c) 2020 Howard Hughes Medical Institute).

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1) Installation

Recommended (PyPI)

Step 1: Open Anaconda Prompt (Windows) or terminal (Linux/macOS)

Create and activate a clean environment:

conda create -n multicellpose python=3.10 -y
conda activate multicellpose

If Conda prompts for Terms of Service acceptance, run:

conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2

Step 2: Install PyTorch first (choose CPU or GPU)

GPU users: check your CUDA version first (Windows):

nvidia-smi

Look for CUDA Version: X.Y. Then install the matching PyTorch wheel:

• CUDA 12.6+:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

• CUDA 12.1:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

• CUDA 11.8:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

CPU-only:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

Step 3: Install MultiCellPose

pip install multicellpose[gui]

This installs GUI, server/client communication dependencies, and image readers (.nd2, .lif, .nrrd) by default.

Step 4: Launch

multicellpose-gui

Alternative launch command:

python -m guv_app.main

From source (developer install)

Use this if you are editing code locally.

Step 1: Download the source code

If you already have Git:

cd C:\Projects
git clone https://github.com/mrcsfltchr/MultiCellPoseRelease.git
cd MultiCellPose

If Git is not installed on Windows, install from https://git-scm.com/download/win first.

Step 2: Create and activate environment

conda create -n multicellpose python=3.10 -y
conda activate multicellpose

If Conda prompts for Terms of Service acceptance, run:

conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2

Step 3: Install PyTorch (CPU or GPU)

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Adjust wheel index URL for your CUDA version, or use CPU wheel.

Step 4: Install repo in editable mode

pip install -e ".[all]"

Step 5: Launch

multicellpose-gui

This opens the Home Navigator with two apps:

• Trainer: labeling + training
• Analyzer: batch inference + measurements

Note: the first time you run the app, pretrained model downloads may take several minutes depending on your network. Critical: ensure your PyTorch CUDA build matches your GPU (older GPUs may require CUDA 11.8/other specific wheels).

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2) Labeling (Trainer)

Load images

• File -> Load image, or drag-and-drop.
• Supported: .tif/.tiff/.png/.jpg/.jpeg/.npy/.nd2/.lif/.dax/.nrrd/.flex.
• .nd2/.lif support is installed by default with both pip install multicellpose and pip install multicellpose[gui].

Create masks

• Right-click once to start a contour, move the cursor to trace the perimeter, then return to the start to close the loop. You do not need to hold right-click while drawing.
• The GUI fills the contour and assigns a new instance ID.
• Brush size: use , and . to decrease/increase.
• Delete a single mask: Ctrl + Left Click.
• Select masks: left-click on a mask or drag a rectangle to select multiple.
• Assign a class to selected masks: Shift + Left Click on a mask.

Create classes and assign

• In the Class Management panel, enter a class name, choose a color, and click Add.
• Select the current class from the dropdown before drawing to assign new masks.
• Use Color by Class to switch between class colors and instance colors.
• Use Delete Masks (Lasso) to remove multiple masks by drawing a closed loop.

Save labels

• File -> Save Masks (as *_seg.npy)
• This saves masks, classes, classes_map, flows, thresholds, and metadata.

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Analyzer (Batch Measurements)

The Analyzer app is for running inference across a folder, optionally correcting masks, and exporting measurements (e.g., area and mean intensity) to CSV.

Load images

• File -> Load image, or drag-and-drop.
• Use the left/right arrow keys to move through a directory after loading.

Load custom models

• Model -> Add custom model.
• Select a .pth or model file in your local models directory.
• The model appears in the model dropdown and can be used for inference.

Connect to remote

• Remote -> Connect to remote.
• Enter your username and password if server requires authentication.
• Once connected, server models appear in the dropdown and inference will run on the remote server.

Upload a trained model to the remote

• Remote -> Upload model to server...
• Select the trained model file you downloaded locally (e.g., .pth).
• The upload can take a few minutes; a progress bar with ETA will appear.
• After upload, the model will appear in the server model dropdown for inference.

Run inference

• Select a model and click Run.
• For batch inference, load a directory and run; masks are saved per-image.
• Note: inference can be slow for image sizes around (512, 512) or larger, especially on CPU.

Correct masks after inference

• Right-click once to draw a new mask (trace a closed contour). You do not need to hold right-click while drawing.
• Delete a single mask with Ctrl + Left Click.
• Use Delete Masks (Lasso) to remove multiple masks by drawing a closed loop.
• In Analyzer, manual edits are saved to the prediction .npy files by default (not the training *_seg.npy files).
• Use the Masks menu to promote predicted masks into training labels when needed.

Run statistics

• Use the Analyzer controls to compute per-object measurements (area, mean intensity, etc.).
• Results are saved to CSV for the full dataset.

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3) Remote connection (SSH + gRPC)

Server setup (remote machine)

1. Install the repo + deps (same as above).
2. Start the gRPC server:

multicellpose-server

By default it listens on 127.0.0.1:50051 (see run_server.py) for secure local-only access. To expose on all interfaces, run:

multicellpose-server --host 0.0.0.0

If you need LAN/WAN exposure, set server_bind explicitly and protect it with firewall rules plus strong auth.

Remote configuration (optional)

You can override server/SSH settings without committing them to Git by creating remote_config.json in the repo root (ignored by git). Use remote_config.example.json as a template. Supported keys include:

• server_bind, server_storage_root
• server_address
• ssh_host, ssh_port, ssh_local_port, ssh_remote_port, ssh_remote_bind
• model_root, replay_root, replay_sample_size
• cpsam_model_path

Connect from the GUI

1. Open the GUI.
2. Use Remote -> Connect to remote.
3. Enter your username and password when prompted.
4. The GUI will create an SSH tunnel to the server and fetch model names.

Manual SSH tunnel (advanced)

ssh -L 50051:localhost:50051 [e-mail]

Then in the GUI, connect to localhost:50051.

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4) Training

Start training (Ctrl+T)

1. Load a folder of labeled images (*_seg.npy, or masks/flows/classes triplets).
2. Press Ctrl+T.
3. Choose a base model and settings.
4. The GUI uploads data to the server (remote mode) or trains locally.
5. Note: training can be slow for large images, especially on CPU.

What happens during training

• The GUI builds a manifest of labeled items in the current folder.
• Remote mode uploads the manifest + required files to the server.
• The server:
  • validates items
  • computes flows (if needed)
  • initializes the model
  • optionally mixes in replay data from a server-side replay dataset
  • trains and saves artifacts into a train_jobs folder

Artifacts include:

• model weights
• training losses (*_train_losses.npy)
• metadata (*_meta.json)

Training with semantic classes from cpsam

If cpsam is selected as the base model and semantic labels exist, the model head is initialized using the exact logic from tools/train_inst_seg_fixed.py so it matches your semantic model architecture (background + classes + flow/cellprob heads).

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Remote storage housekeeping

If you hit your remote quota, use:

Remote -> Clear remote training files...

This removes all previous training job files for your user on the server.

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Troubleshooting

• CUDA not available: install a PyTorch CUDA wheel that matches your GPU.
• Missing nd2/readlif: reinstall package dependencies (pip install --upgrade multicellpose) and restart.
• Remote connection fails: confirm the server is running and port 50051 is reachable.
• No labels detected: ensure files are *_seg.npy or matching _masks/_flows/_classes triplets.

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Developer docs

Image format loader guide

To add a new image format, implement a custom ImageReader and register it in cellpose/io.py. The GUI and services call the reader through the shared interface, so the app stays format-agnostic.

Requirements:

• Provide read(...) and iter_frames(...). If your format supports random access, implement read_frame(...) and get_series_time_info(...) for fast navigation.
• Populate ImageMeta with accurate axes, shape, and sizes when possible.
• Return channel-last arrays (YXC) for multi-channel 2D images.
• Use frame_id values like S0, S0_T1, or T3 for series/time indexing.

Implementation steps:

1. Add a reader class in cellpose/io.py (see _Nd2Reader and _LifReader).
2. Implement extensions for your file suffixes.
3. Register it with register_reader(...).
4. If your format can be large, implement read_frame(...) and get_series_time_info(...) to avoid loading the full file.

Testing:

• Add/extend tests in tests/test_io_*.py.
• Validate that arrow-key navigation works for multi-frame files.

Custom plugin guide

Plugins live under guv_app/plugins and follow a standard interface. The Analyzer discovers and registers them at startup.

Requirements:

• Implement the base interface in guv_app/plugins/interface.py.
• Provide a unique name, optional config schema, and a run(...) method.
• Keep plugins pure: do not mutate GUI state directly. Return results to the controller via the plugin interface.

Implementation steps:

1. Create a new plugin module in guv_app/plugins/.
2. Inherit from the plugin base class and implement required methods.
3. If configuration is needed, add a config UI via guv_app/views/dialogs/plugin_config_dialog.py.
4. Ensure results are serializable for CSV export.

Testing:

• Add a unit test in guv_app/tests/ using existing plugin tests as templates.
• Run the Analyzer and verify the plugin appears in the dropdown and produces output.

Experimental: LLM plugin-creator skill (in development)

An in-repo LLM skill is available to help scaffold new Analyzer plugins:

• Skill file: llm_skills/make-custom-plugin/SKILL.md
• Template: llm_skills/make-custom-plugin/templates/plugin_template.py

Status:

• This feature is experimental and currently in development.
• Generated plugins should always be reviewed manually before use.

Suggested workflow:

1. Use the skill to generate a new plugin module under guv_app/plugins/.
2. Confirm it implements the interface in guv_app/plugins/interface.py.
3. Validate plugin contracts:

python scripts/validate_plugins.py

4. Add/adjust tests in guv_app/tests/ and verify in the Analyzer UI.

Validator utility files:

• guv_app/plugins/plugin_validator.py
• scripts/validate_plugins.py

Using this skill in coding agents

Codex:

1. Keep it project-local (current setup): llm_skills/make-custom-plugin/.
2. Or install globally by copying the folder into your Codex skills directory (for example under $CODEX_HOME/skills/), then reference it by name in your prompt.
3. When invoking the skill, include the plugin objective, required output columns, and parameter definitions.

Claude coding agents:

1. Claude does not use Codex SKILL.md as a guaranteed native standard.
2. Use the same content as an agent instruction file (for example in CLAUDE.md) or paste the workflow section into your prompt template.
3. Keep the plugin template file (llm_skills/make-custom-plugin/templates/plugin_template.py) in the repo and instruct Claude to generate plugins against guv_app/plugins/interface.py.

Validation (recommended for any agent-generated plugin):

python scripts/validate_plugins.py

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If you want additional documentation (Analyzer batch measurement workflow, data formats, or deployment notes), ask and I will expand this README.

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Acknowledgements

This project builds on the Cellpose project. Please see the Cellpose documentation for additional GUI usage details and background information: https://www.cellpose.org/ and https://cellpose.readthedocs.io/

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HPC cluster install and CLI training (GPU)

Use this when you want to run training jobs non-interactively on a shared GPU cluster.

1) Prepare environment on the cluster

From a login node:

1. Load your cluster modules as needed (example):

module load anaconda
module load cuda/12.1

2. Clone the repo:

git clone https://github.com/mrcsfltchr/MultiCellPoseRelease.git
cd MultiCellPose

3. Create and activate a conda env:

conda create -n multicellpose python=3.10 -y
conda activate multicellpose

4. If required by your cluster Conda setup, accept Terms of Service:

conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2

5. Install PyTorch with the correct CUDA wheel for the cluster GPUs:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

6. Install MultiCellPose:

pip install -e ".[all]"

2) Run headless training from CLI

The repo includes a headless trainer:

• train_headless.py

Example:

python train_headless.py \
  --train-dir /path/to/train_dataset \
  --test-ratio 0.2 \
  --base-model cpsam \
  --model-name cpsam_cluster_run01 \
  --epochs 50 \
  --batch-size 10 \
  --bsize 256 \
  --learning-rate 5e-5 \
  --weight-decay 0.1 \
  --use-lora \
  --lora-blocks 4 \
  --save-path /path/to/output_models

Notes:

• Use --test-dir /path/to/test_dataset instead of --test-ratio if you have a separate test set.
• For non-LoRA fine-tuning, remove --use-lora and set --unfreeze-blocks.
• Add --train-debug --train-debug-steps 3 to log per-batch timing/memory diagnostics.

3) Example Slurm batch script

Save as train_job.slurm:

#!/bin/bash
#SBATCH --job-name=multicellpose-train
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G
#SBATCH --time=24:00:00
#SBATCH --output=logs/%x-%j.out

set -euo pipefail

module load anaconda
module load cuda/12.1

source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate multicellpose

cd /path/to/MultiCellPose

python train_headless.py \
  --train-dir /path/to/train_dataset \
  --test-ratio 0.2 \
  --base-model cpsam \
  --model-name cpsam_slurm_run01 \
  --epochs 50 \
  --batch-size 10 \
  --bsize 256 \
  --use-lora \
  --lora-blocks 4 \
  --save-path /path/to/output_models

Submit with:

sbatch train_job.slurm

Local/Remote Parity TODO

The project is actively consolidating duplicated local and remote code paths.
Completed: class-map parsing/sanitization and trainability configuration (LoRA/unfreeze) are now shared.

Remaining parity tasks:

• Unify semantic postprocessing for inference (class-map extraction, resize, per-mask class voting) across:
  • local: guv_app/services/segmentation_service.py
  • remote: cpgrpc/server/services.py
• Unify dataset/label loading and frame expansion logic across:
  • guv_app/services/training_dataset_service.py
  • guv_app/workers/remote_training_worker.py
  • cpgrpc/server/services.py
• Unify training defaults/parameter canonicalization (normalize params, test split semantics, min mask handling) between:
  • training dialog/controller + local worker
  • remote manifest builder + server trainer
• Reduce local duplicate training loop implementation in guv_app/services/training_service.py by reusing shared cellpose/train.py entry points where possible.

PyPI Packaging and Release (Draft)

This repository now includes:

• pyproject.toml (PEP 621 metadata + setuptools_scm)
• CI packaging checks: .github/workflows/ci.yml
• PyPI publish workflow: .github/workflows/publish-pypi.yml

Build locally

From the repo root:

python -m pip install --upgrade pip build twine
python -m build
python -m twine check dist/*

Windows install test (NVIDIA GPU, CUDA 12.1)

From the repo root, use the helper script:

powershell -ExecutionPolicy Bypass -File .\scripts\test_install_windows_cuda121.ps1

This script:

• creates a fresh conda env
• accepts conda ToS channels
• installs PyTorch cu121 wheels
• installs MultiCellPose (.[all] by default)
• verifies torch CUDA visibility
• smoke-tests the CLI/import entry points

To test a built wheel instead of editable source:

python -m build
powershell -ExecutionPolicy Bypass -File .\scripts\test_install_windows_cuda121.ps1 -PackageSpec ".\dist\multicellpose-<VERSION>-py3-none-any.whl"

Publish from GitHub Actions (Trusted Publishing)

1. Create a PyPI project (name currently set to multicellpose in pyproject.toml).
2. In PyPI project settings, add a Trusted Publisher:
   • Owner: your GitHub org/user
   • Repository: mrcsfltchr/MultiCellPose
   • Workflow: publish-pypi.yml
   • Environment: pypi
3. In GitHub repository settings, create environment pypi.
4. Create a GitHub Release (tag), which triggers publish.

The publish workflow uses OIDC and does not require a PyPI API token secret.