Installation#

Requirements#

Python 3.12–3.13
PyTorch ≥ 2.0
NumPy ≥ 2.0
zarr ≥ 3.0
Cellpose ≥ 4.0
CAREamics ≥ 0.0.9

Development install#

Clone the repository and install with Poetry:

git clone https://github.com/dv-bt/sphero-vem.git
cd sphero-vem
poetry install           # CPU and limited GPU acceleration
poetry install -E cuda   # with CUDA 12.x GPU acceleration (Linux only)

GPU acceleration#

PyTorch-based stages (registration, segmentation) support GPU execution via PyTorch’s native device management, including CUDA and MPS (Apple Silicon), with no additional dependencies.

Array operation stages (denoising, shape analysis, spatial analysis) additionally support CUDA acceleration via CuPy and CuCIM, installed with the cuda optional dependency group above. Automatic backend switching (NumPy ↔ CuPy, scikit-image ↔ CuCIM) is handled at import time by utils.accelerator.

Dataset and model weights#

The annotated SBF-SEM dataset used to develop and benchmark this pipeline is available at BioImage Archive. Fine-tuned Cellpose-SAM model weights are available on Zenodo.