Skip to main content

cellcast_python

pypi license

This crate contains the Python bindings (via PyO3) for the cellcast core Rust library. Cellcast is a recast of cell segmentation models built on the Burn tensor and deep learning framework. The goal of this project is to modernize (i.e. recast) established cell segmentation models with a WebGPU backend. Cellcast aims to make access to cell segmentation models easy and reproducible.

Installation

Requirements

The cellcast Python package currently supports the following architectures:

Operating System Architecture
Linux x86-64, arm64
macOS intel, arm64
Windows x86-64

Cellcast is compatible with Python >=3.8 and requires only NumPy.

cellcast from PyPI

You can install the cellcast Python package from PyPI with:

$ pip install cellcast

Build cellcast_python from source

To build the cellcat_python package from source, use the maturin build tool (this requires the Rust toolchain). If you're using uv to manage your Python virtual environments (venv) add numpy and maturin to your environment and run the maturin develop command in the cellcast_python directory of the cellcast repository with your venv activated:

$ source ~/path/to/myenv/.venv/bin/activate
$ (myenv) cd cellcast_python
$ maturin develop

Alernatively if you're using conda or mamba you can do the following:

$ cd cellcat_python
$ mamba activate myenv
(myenv) $ mamba install numpy maturin
...
(myenv) $ maturin develop

This will compile a non-optimized cellcast binaries. Pass the --release flag to compile optimized binaries (note that compilation time may take upwards of 10 minutes).

Usage

Using cellcast

The following example demonstrates how to use cellcast's StarDist2D model in Python with fetched versatile fluo pre-trained weights (note: here we assume you have your data in a 2D NumPy array):

from cellcast.models import StarDist2D

# assuming "data" is a 2D NumPy array
sd = StarDist2D.init_fluo(gpu=True)
labels = sd.predict_fluo(data)

Run help() on the predict_fluo() function to see the full function signature and default values. To initialize a model with custom weights, provide the path to the weights in burnpack format (.bpk) when creating a model instance.

sd = StarDist2D.init_fluo("path/to/custom_weights.bpk", True)

License

Cellcast itself is a dual-licensed project with your choice of:

These licenses only apply to the cellcast project and do not apply to the individual models supported by cellcast. You can find each model's associated license listed in the MODEL-LICENSES file.

Metadata

Release files for cellcast 0.3.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cellcast 0.3.1
File Size Uploaded Provenance
cellcast-0.3.1.tar.gz 81.4 kB With provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for cellcast 0.3.1
File
cellcast-0.3.1-cp38-abi3-win_amd64.whl CPython 3.8 abi3 Windows x86-64 Details
cellcast-0.3.1-cp38-abi3-manylinux_2_28_aarch64.whl CPython 3.8 abi3 Linux glibc 2.28+ ARM64 Details
cellcast-0.3.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.8 abi3 Linux glibc 2.17+ x86-64 Details
cellcast-0.3.1-cp38-abi3-macosx_11_0_arm64.whl CPython 3.8 abi3 macOS 11.0+ ARM64 Details
cellcast-0.3.1-cp38-abi3-macosx_10_12_x86_64.whl CPython 3.8 abi3 macOS 10.12+ x86-64 Details

Total release size: 58.9 MB

Release files / cellcast-0.3.1.tar.gz

Download URL cellcast-0.3.1.tar.gz
Size 81.4 kB
Tags Source
SHA-256 checksum
How to use checksums
273520473e017dbd8ecd9aefba471ad7dd2a181c01b0f12d87e62a4f8634449e
BLAKE2b-256 checksum
How to use checksums
a6e40ff108cd689fc82188142692a06ef48719822029835a7676895a920cceb4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Release files / cellcast-0.3.1-cp38-abi3-win_amd64.whl

Download URL cellcast-0.3.1-cp38-abi3-win_amd64.whl
Size 12.6 MB
Tags CPython 3.8 Windows x86-64 abi3
SHA-256 checksum
How to use checksums
5dd1b921e8007fcb12ef793127f29ed9f7abd35c5781a1a46d8ef67cddbaa833
BLAKE2b-256 checksum
How to use checksums
8c0da98fa3e46db83f3e8a6d28be32343e120eb7affc8f9ced9a5aa8036c6d3f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Release files / cellcast-0.3.1-cp38-abi3-manylinux_2_28_aarch64.whl

Download URL cellcast-0.3.1-cp38-abi3-manylinux_2_28_aarch64.whl
Size 11.5 MB
Tags CPython 3.8 Linux glibc 2.28+ ARM64 abi3
SHA-256 checksum
How to use checksums
966fee7268e8ef38724aa5d8a5aafaaf3b3833cdfedd73f22176b7f7f633bc5a
BLAKE2b-256 checksum
How to use checksums
aa24ba107fbee1d2d12f5d138039b95570f8a3b3f943b383331a4678aaa4ce28
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Release files / cellcast-0.3.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL cellcast-0.3.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 12.0 MB
Tags CPython 3.8 Linux glibc 2.17+ x86-64 abi3
SHA-256 checksum
How to use checksums
464043978ea367ce801cd55f9e36be9b0912465dc0285793ad65d90b0cde535c
BLAKE2b-256 checksum
How to use checksums
57c47a39adb49f0d2f4d236f1546463016b68b583214f539673c38e7aa3bd770
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Release files / cellcast-0.3.1-cp38-abi3-macosx_11_0_arm64.whl

Download URL cellcast-0.3.1-cp38-abi3-macosx_11_0_arm64.whl
Size 10.9 MB
Tags CPython 3.8 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
1f29990a1d3abb6e4e278d2543402c23bf8783034eb2d75f030a8a0967f45e80
BLAKE2b-256 checksum
How to use checksums
3c43d35d3f3f325c8b9cba287d07f8711c8fe75538743b0781c6112f055c9c0b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Release files / cellcast-0.3.1-cp38-abi3-macosx_10_12_x86_64.whl

Download URL cellcast-0.3.1-cp38-abi3-macosx_10_12_x86_64.whl
Size 11.8 MB
Tags CPython 3.8 abi3 macOS 10.12+ x86-64
SHA-256 checksum
How to use checksums
41053bdb3f5798b08d684e3920234e89c4ba4daf47e73ebb33137c47a2802e00
BLAKE2b-256 checksum
How to use checksums
13114ac7cfca8b497be72b1f9b8e921f9ef89e11c73b8ac12bafd7121ad680ec
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

Security for cellcast 0.3.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 6 files in this release have been uploaded with provenance metadata.

Select from 6 files

Attestation for cellcast-0.3.1.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page