Cellpose is a sophisticated deep learning-based algorithm designed for the accurate segmentation of cells in microscopy images. Developed at the Janelia Research Campus, this tool stands out due to its generalist approach, allowing it to precisely identify and outline a wide array of cell types and shapes without requiring extensive model retraining for each new dataset. It processes images by predicting the shape of objects using a flow-representation of cell dynamics, which effectively captures the complex borders of cells. This capability makes Cellpose a valuable resource for researchers and scientists in various biological applications, providing consistent and reliable segmentation results across diverse imaging modalities.
Navigating the GitHub Repository
For users interested in the Cellpose project, its primary development hub is hosted on GitHub, accessible at `https://github.com/MouseLand/cellpose`. This repository serves as the central location for the software’s source code, documentation, and community interactions.
A particularly important file within the repository is the `README.md`, which offers a quick-start guide, installation instructions, and an overview of the project’s features. Users can also find the “Issues” tab, a dedicated section for reporting bugs, submitting feature requests, and tracking known problems that developers are actively addressing. Additionally, the “Releases” section provides access to different versions of the software, allowing users to download specific stable builds or review historical updates.
Installation and Setup
Cellpose can be installed using `pip` or `conda`. Before proceeding, it is important to have Python version 3.8 or later installed on your system; Python 3.10 is generally recommended for optimal compatibility. The choice between `pip` and `conda` often depends on existing Python environments and user preference.
For `pip` installation, create a virtual environment (e.g., `python3 -m venv cellpose`) and activate it. Then, install Cellpose with `python -m pip install cellpose` or `python -m pip install ‘cellpose[gui]’` for the graphical user interface. Alternatively, `conda` users can create and activate a new environment (`conda create –name cellpose python=3.10`, `conda activate cellpose`), then install Cellpose using the same `pip` commands.
Regarding hardware, Cellpose can run on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). However, for significantly faster performance, especially when processing large datasets or training custom models, a compatible NVIDIA GPU with correctly installed CUDA libraries is highly recommended. While CPU-only installations are functional, they may result in longer processing times compared to GPU-accelerated operations.
Core Functionality and Usage
Cellpose can be used via its Graphical User Interface (GUI) or through command-line interface and Python scripting. The GUI offers an intuitive visual workspace for image segmentation, which can be launched by typing `python -m cellpose` in your activated environment’s terminal. Within the GUI, users can load images by simply dragging and dropping common file types such as .tif, .png, .jpg, or .gif.
The GUI provides features for running the segmentation algorithm and manually refining masks, including tools for drawing new regions of interest (ROIs) with a right-click or deleting incorrect ones with a Ctrl+click. After segmentation, the results are saved as `_seg.npy` files in the same directory as the original image. Cellpose includes several pre-trained models, such as ‘cyto’ and ‘cyto2’ for cytoplasmic or whole-cell segmentation, which can often be enhanced by providing a second nuclear channel. The ‘nuclei’ model is specifically designed for segmenting cell nuclei.
For automated workflows or batch processing, Cellpose can be operated from the command line or within Python scripts. This method allows for greater flexibility and integration into larger analytical pipelines. Users can specify the input directory, desired pre-trained model, and output options using commands like `python -m cellpose –dir /path/to/images –model_type cyto –channels 0 0` for grayscale images, or `–channels 2 3` for segmenting green cytoplasmic cells with blue nuclei.
Training Custom Models
While Cellpose’s pre-trained models are highly versatile, there are instances where a user’s specific cell types or imaging conditions may require a more tailored solution. This is where training custom models becomes beneficial, allowing the algorithm to adapt to unique data characteristics. The process involves providing Cellpose with training data, which consists of matched pairs of raw images and their corresponding manually created segmentation masks.
These training image files and their associated masks typically follow a specific naming convention, such as `image_name.tif` for the raw image and `image_name_masks.tif` for its mask counterpart, or similar suffixes like `_imRGB.png` and `_mask.png`. Users can prepare this data using the Cellpose GUI itself, where they can manually refine segmentation outlines and save them for training purposes. Once a dataset of annotated images is ready, custom model training can be initiated through the GUI’s “Models” menu by selecting “Train new model,” or by executing a specific Python command-line script pointing to the directories containing the prepared images and masks. This iterative process of annotating and retraining helps the model learn the specific features of the user’s cells, often leading to improved segmentation accuracy for subsequent analyses.
Community and Support
The Cellpose project fosters an active community, providing multiple avenues for users to seek assistance and contribute. For specific issues, such as reporting software bugs or suggesting new functionalities, the GitHub “Issues” tab on the official repository serves as the primary channel. Developers and other users actively monitor this section, offering solutions and tracking the progress of reported problems.
For more general questions, discussions about best practices, troubleshooting common challenges, or sharing segmentation results, the image.sc forum is a highly recommended resource. This platform is a broad community hub for bioimage analysis, where Cellpose developers and experienced users frequently engage, providing detailed advice and fostering collaborative problem-solving. Engaging with these community resources can greatly assist users in maximizing their Cellpose experience and contribute to the ongoing improvement of the software.