A scalable implementation of WobblyStitcher for 3D microscopy images

Related tags

Image Processing visualization python multiprocessing numpy parallel medical-imaging image-analysis microscopy-images 3d-images segmentation-pipeline
Overview

WobblyStitcher

Introduction

A scalable implementation of WobblyStitcher

Dependencies

$ python -m pip install numpy scikit-image

Visualization

ImageJ

Getting started

Generate four files with fake input

$ (cd tool && make)
$ ./tool/gen -n 200 200 200 -o 10 10
$ ls -1 *.raw
200x200x200le.00.00.raw
200x200x200le.00.01.raw
200x200x200le.01.00.raw
200x200x200le.01.01.raw

fake input

stitch

$ python3 main.py
main.py: processes = 4
47% 390x390x200le.raw

Open 390x390x200le.raw in ImageJ.

sample output

Similar programs

BigStitcher
TeraStitcher
ClearMap

References

  1. Kirst, C., Skriabine, S., Vieites-Prado, A., Topilko, T., Bertin, P., Gerschenfeld, G., ... & Renier, N. (2020). Mapping the fine-scale organization and plasticity of the brain vasculature. Cell, 180(4), 780-795.
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Comments
  • Serialize part of the stitching

    Serialize part of the stitching

    Serialize part of the stitching

    In the wobbly stitching, put everything with high RAM demands into a separate, non-parallelized function align_pair_serial().

    opened by PTRRupprecht 0
Owner
CSE Lab, ETH Zurich
CSE Lab, ETH Zurich
GitHub
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