stainwarpy 0.1.0

Tools for image registration between multiplexed and HnE stained tissue images

stainwarpy

stainwarpy is a command-line tool and a Python package for registering H&E stained and multiplexed tissue images. It provides a feature based registration pipeline, saving registered images, transformation maps and evaluation metrics.

Features

• Register H&E images and multiplexed images (after extracting DAPI channel) using transformations.
• Supports feature-based registration.
• Outputs registered images (in the pixel size of moving image), transformation maps and evaluation metrics (TRE and Mutual Information).
• Transforms segmentation masks based on the computed transformations

Recommendations

• For most cases, it is recommended to register H&E images onto multiplexed images (H&E as moving image).
• The default similarity transformation usually works well and stable, therefore recommended.

Installation

You can install stainwarpy using pip:

pip install stainwarpy

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Usage as a command-line tool

Register Images

stainwarpy register <fixed_path> <moving_path> <output_folder> <fixed_img> [options]

Examples:

stainwarpy register data/fixed_img.ome.tiff data/moving_img.ome.tiff ../output multiplexed

stainwarpy register data/fixed_img.tif data/moving_img.tif ../output multiplexed --fixed-px-sz 0.21 --moving-px-sz 0.52

Arguments:

• fixed_path: Path to the fixed image (H&E or DAPI or Multiplexed image path (.tif/.tiff./.ome.tif/.ome.tiff))
• moving_path: Path to the moving image (H&E or DAPI or Multiplexed image path (.tif/.tiff./.ome.tif/.ome.tiff))
• output_folder: Folder to save the registered images and metrics
• fixed_img: Type of fixed image: multiplexed or hne

Options:

• --fixed-px-sz : Pixel size of the fixed image (no need to provide for ome.tiff, so default: None)
• --moving-px-sz : Pixel size of the moving image (no need to provide for ome.tiff, so default: None)
• --feature-tform : Feature transformation method: similarity oraffine or projective (default: similarity)

Output

After running registration, the following files/folders will be generated:

• results/registration_metrics.json — TRE and Mutual Information
• results/0_final_channel_image.tif — Registered image (in the pixel size of moving image)
• results/feature_based_transformation_map.npy — Transformation map

Extract a Channel (DAPI can be extracted for registration)

stainwarpy extract-channel <file_path> <output_folder_path> [--channel-idx N]

Arguments

• file-path* : Path to multichannel image (.tif/.tiff/.ome.tif/.ome.tiff)
• output-folder-path : Folder to save the extracted channel image

Options

• --channel-idx: Channel index to extract (default: 0 for DAPI)

Output

• /multiplexed_channel_{channel_idx}.tif - Image with the extracted channel

Transform segmentation Masks

Transform segmentation masks based on the transformation maps produced with the command register.

stainwarpy transform-seg-mask <mask_path> <fixed_path> <output_folder_path> <tform_map_path> <moving_px_sz> [--fixed-px-sz]

Arguments

mask_path : Path to the segmentation mask of the moving image (.npy) fixed_path : Path to the fixed image (.tif/.tiff/.ome.tif/.ome.tiff) output_folder_path : Folder to save the transformed segmentation mask tform_map_path : Path to the transformation map moving_px_sz : Pixel size of the moving image (no need to provide for ome.tiff, so default: None)

Options

• --fixed-px-sz : Pixel size of the fixed image (no need to provide for ome.tiff, so default: None)

Output

• /transformed_segmentation_mask.npy : The segmentation mask transformed to the fixed image coordinate space

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Usage as a Python Library

Although stainwarpy is mainly a command-line tool, its functions can also be used directly in Python for scripting.

Example: Running the Registration Pipeline

from stainwarpy.regPipeline import registration_pipeline

# run registration pipeline
tform_map, final_img, tre, mi = registration_pipeline(
    fixed_path="fixed_image.tif",
    moving_path="moving_image.tif",
    fixed_px_sz=0.5,
    moving_px_sz=0.5,
    fixed_img="multiplexed",
    feature_tform="affine"        # used if adv_tform= is not "similarity"
)

print("TRE:", tre)
print("Mutual Information:", mi)

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License

This project is licensed under the MIT License.