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PlatyMatch allows registration of volumetric images of embryos by establishing correspondences between cells

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DOI:10.1007/978-3-030-66415-2_30 License: MIT PyPI Python Version tests codecov

Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence

Table of Contents


This repository hosts the version of the code used for the publication Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence.

We refer to the techniques elaborated in the publication, here as PlatyMatch. PlatyMatch performs a linear registration of volumetric, microscopy images of embryos by establishing correspondences between cells.

PlatyMatch first detects nuclei in the two images being considered, next calculates unique shape context features for each nucleus detection which encapsulates the neighborhood as seen by that nucleus, and finally identifies pairs of matching nuclei through maximum bipartite matching applied to the pairwise distance matrix generated from these features.


You can install PlatyMatch via pip:

conda create -y -n PlatyMatchEnv python==3.8
conda activate PlatyMatchEnv
python3 -m pip install PlatyMatch

Getting Started

Type in the following commands in a new terminal window.

conda activate PlatyMatchEnv

Next, select PlatyMatch from Plugins> Add Dock Widget.


Datasets are available in as release assets here. These comprise of images, nuclei detections and keypoint locations for confocal images of 12 individual specimens under the 01-insitus directory and static snapshots of a live embryo imaged through Light Sheet Microscopy under the 02-live directory. Folders with the same name in these two directories correspond in their developmental age, for example, 01-insitus/02 corresponds to 02-live/02, 01-insitus/03 corresponds to 02-live/03 and so on.

Registering your data

  • Detect Nuclei
    • Drag and drop your images in the viewer
    • Click on Sync with Viewer button to refresh the drop-down menus
    • Select the appropriate image in the drop down menu (for which nuclei detections are desired)
    • Select Detect Nuclei from the drop-down menu
    • Specify the anisotropy factor (Anisotropy (Z)) (i.e. the ratio of the size of the z pixel with respect to the x or y pixel. This factor is typically more than 1.0 because the z dimension is often undersampled)
    • Ideally min scales and max scales should be estimated from your data (min_scale should be set as min_radius/sqrt(3) and max_scale should be set as max_radius/sqrt(3). The default values of min_scale=5 and max_scale=9 generally works well).
    • Click Run Scale Space Log button. Please note that this step takes a few minutes.
    • Wait until a confirmation message suggesting that nuclei detection is over shows up on the terminal
    • Export the nuclei locations (Export detections to csv) to a csv file
    • Repeat this step for all images which need to be matched

  • Estimate Transform
    • In case, nuclei were exported to a csv in the Detect Nuclei panel, tick csv checkbox
    • If the nuclei detected were specified in the order id, z, y and x in the csv file, then tick IZYXR checkbox
    • Additionally if there is a header in the csv file, tick Header checkbox
    • Load the detections for the Moving Image, which is defined as the image which will be transformed to later match another fixed image
    • Load the detections for the Fixed Image
    • Click on Run pushbutton. Once the calculation is complete, a confirmation message shows up in the terminal. Export the transform matrix to a csv (Note that this step can take a few minutes)
    • It is also possible to estimate the transform in a supervised fashion. For this, upload the locations of a few matching keypoints in both images. These locations serve to provide a good starting point for the transform calculation. Once the keypoint files have been uploaded for both the images, then click Run and then export the transform matrix to a csv file

  • Evaluate Metrics
    • Drag images which need to be transformed, in the viewer
    • Click on Sync with Viewer button to refresh the drop-down menus
    • Specify the anisotropy factor (Moving Image Anisotropy (Z) and Fixed Image Anisotropy (Z)) (i.e. the ratio of the size of the z pixel with respect to the x or y pixel. This factor is typically more than 1.0 because the z dimension is often undersampled)
    • Load the transform which was calculated in the previous steps
    • If you simply wish to export a transformed version of the moving image, click on Export Transformed Image
    • Additionally, one could quantify metrics such as average registration error evaluated on a few keypoints. To do so, tick the csv checkbox, if keypoints and detections are available as a csv file. Then load the keypoints for the moving image (Moving Kepoints) and the fixed image (Fixed Keypoints)
    • Also, upload the detections calculated in the previous steps (Detect Nuclei) by uploading the Moving Detections and the Fixed Detections
    • Click on the Run push button
    • The text fields such as Matching Accuracy(0 to 1, with 1 being the best) and Average Registration Error (the lower the better) should become populated once the results are available


Contributions are very welcome. Tests can be run with tox.


If you encounter any problems, please file an issue along with a detailed description.


If you find our work useful in your research, please consider citing:

author="Lalit, Manan and Handberg-Thorsager, Mette and Hsieh, Yu-Wen and Jug, Florian and Tomancak, Pavel",
editor="Bartoli, Adrien
and Fusiello, Andrea",
title="Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence",
booktitle="Computer Vision -- ECCV 2020 Workshops",
publisher="Springer International Publishing",

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