cellstitch-cuda 1.0.1

CUDA-accelerated CellStitch 3D labeling using Instanseg segmentation.

CellStitch-Cuda: CUDA-accelerated CellStitch 3D labeling.

About this repo

An overhaul of the CellStitch algorithm, developed by Yining Liu and Yinuo Jin (original repository), publication can be found here.

Some major adjustments:

• Replaced NumPy with CuPy for GPU-accelerated calculations.
• Replaced nested for-loops with vectorized calculations for dramatic speedups (~100x).
• Included novel segmentation method InstanSeg, which enables multichannel inputs (repo and publication).
• An all-in-one method that takes an ZCYX-formatted .tif file, performs the correct transposes, and writes stitched labels.
• Included a histogram-based bleach correction to adjust for signal degradation over the Z-axis (originally developed for ImageJ in (Miura 2020) and released for Python by marx-alex in napari-bleach-correct).

Some comparisons

The calculations were run on the same machine (GPU: NVIDIA Quadro RTX 6000 24 GB; CPU: Intel Xeon Gold 6252 (48/96 cores); RAM: 1024 GB), the core count of which gave it a clear parallel-processing advantage. This particularly affects the fill_holes_and_remove_small_masks function, which has been rewritten to utilize parallel processing.

In image A, GPU VRAM load was ~200 MB at its peak.

In image B, GPU VRAM load was ~2442 MB at its peak

Installation

Notes

This setup has so far only been verified on Windows-based, CUDA-accelerated machines. Testing has only been performed on CUDA 12.x. There are no reasons why 11.x should not work (check instructions), but your mileage may vary.

Conda setup

conda create -n cellstitch-cuda python=3.9
conda activate cellstitch-cuda

Install using PyPi

pip install cellstitch-cuda
conda install pytorch pytorch-cuda=12.1 -c conda-forge -c pytorch -c nvidia

You may replace the version number for pytorch-cuda with whatever is applicable for you.

Additional steps for CUDA 11.x

pip uninstall cupy-cuda12x
pip install cupy-cuda11x

Instructions

From an image

from cellstitch_cuda.pipeline import cellstitch_cuda

img = "path/to/image.tif"
# or feed img as a numpy ndarray

3d_masks = cellstitch_cuda(img)

From pre-existing orthogonal labels

from cellstitch_cuda.pipeline import full_stitch

# Define xy_masks, yz_masks, xz_masks in some way

3d_masks = full_stitch(xy_masks, yz_masks, xz_masks)

References

Goldsborough, T., O’Callaghan, A., Inglis, F., Leplat, L., Filbey, A., Bilen, H., & Bankhead, P. (2024) A novel channel invariant architecture for the segmentation of cells and nuclei in multiplexed images using InstanSeg. bioRxiv, 2024.09.04.611150. doi: 10.1101/2024.09.04.611150

Liu, Y., Jin, Y., Azizi, E., & Blumberg, E. (2023) Cellstitch: 3D cellular anisotropic image segmentation via optimal transport. BMC Bioinformatics, 24(480). doi: 10.1186/s12859-023-05608-2

Miura, K. (2020) Bleach correction ImageJ plugin for compensating the photobleaching of time-lapse sequences. F1000Res, 9:1494. doi: 10.12688/f1000research.27171.1

Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) Cellpose: a generalist algorithm for cellular segmentation. Nature Methods, 18(1), 100-106. doi: 10.1038/s41592-020-01018-x