ShapeSpaceExplorer is a software package in MATLAB. It uses a machine learning approach to understand the relationship of cell shape dynamics and cell migration behaviour. Our algorithm analyses cell shape from image timelapse sequences and learns the intrinsic low-dimensional structure of cell shape space. The resultant shape space map can be used to visualise differences in cell shape distribution following perturbation experiments and to analyse the quantitative relationships between shape and migration behaviour. The core of our software is a new, rapid, landmark-free shape difference measure that allows unbiased analysis of the widely varying morphologies exhibited by migrating epithelial cells.
The software has been tested with Matlab R2022b and 2024b on Windows 10 and requires the following toolboxes:
- Image Processing
- MATLAB Coder
- MATLAB Compiler
- Statistics and Machine Learning
- Deep Learning (optional, only required for group analysis of regions)
Copyright (C) 2025 Samuel Jefferyes, Roswitha Gostner, Laura Cooper, Mohammed Abdelsamea and Anne Straube
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 2. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
To learn how to use ShapeSpaceExplorer, please read the documentation.
If you use our software please cite our paper:
Samuel D.R. Jefferyes, Roswitha Gostner, Laura Cooper, Mohammed M Abdelsamea, Elly Straube, Nasir Rajpoot, David B.A. Epstein, and Anne Straube (2026). ShapeSpaceExplorer: Analysis of morphological transitions in migrating cells using similarity-based shape space mapping. PLoS Comput Biol 22(1): e1013864. https://doi.org/10.1371/journal.pcbi.1013864
Please submit an issue or email camdu@warwick.ac.uk
The developers of ShapeSpaceExplorer are grateful to the developers of the following tools which are used in this software package:
Melissa Linkert, Curtis T. Rueden, Chris Allan, Jean-Marie Burel, Will Moore, Andrew Patterson, Brian Loranger, Josh Moore, Carlos Neves, Donald MacDonald, Aleksandra Tarkowska, Caitlin Sticco, Emma Hill, Mike Rossner, Kevin W. Eliceiri, and Jason R. Swedlow (2010) Metadata matters: access to image data in the real world. The Journal of Cell Biology 189(5), 777-782. doi: 10.1083/jcb.201004104
Shai Bagon, Weizmann Centre of Artificial Intelligence
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C. Christoudias, B. Georgescu, P. Meer: "Synergism in low level vision". 16th International Conference of Pattern Recognition, Track 1 - Computer Vision and Robotics, Quebec City, Canada, August 2001.
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