Improved giga-pixel WSI registration through automated segment-based registration. Proof-of-concept pipeline, would love your contributions!
Currently under review and additional validation, Biorxiv: https://www.biorxiv.org/content/10.1101/2020.03.22.002402v1
UPDATE: We are in the process of updating this repository with the necessary level of documentation and adding a Wiki page, stay tuned!
This package can be installed for Python 3.6+ using the following command:
pip install pathflow_mixmatch
Our latest package build can be installed using:
pip install git+https://github.com/jlevy44/PathFlow-MixMatch.git
Minimal working example:
pathflow-mixmatch register_images --im1 A.png --im2 B.png --fix_rotation False --output_dir output_registered_images/ --gpu_device 0 --transform_type similarity --lr 0.01 --iterations 1000 --min_object_size 50000
To run without the segment based analysis, if images have black background (eg. using HistoQC) and training on the CPU:
pathflow-mixmatch register_images --im1 A.png --im2 B.png --fix_rotation False --output_dir output_registered_images/ --gpu_device -1 --transform_type similarity --lr 0.01 --iterations 1000 --min_object_size 50000 --no_segment_analysis True --black_background True
See https://airlab.readthedocs.io/ for further description of available transformations and loss functions.
Currently available loss functions:
- mse
- ncc
- lcc
- mi
- mgf
- ssim
Currently available transformations:
- similarity
- affine
- rigid