Welcome to the GitHub repository of the Cell Migration Lab! Our lab is dedicated to understanding cell migration, focusing on cancer research. We also develop image analysis tools to advance this field.
The cell migration lab is located in Turku, Finland. The lab started in 2019 and is part of the Cell Biology department at Åbo Akademi University. We are part of the Solution for Health research profile and INFLAMES Flagship. We are affiliated with Turku Bioscience Centre and use their core facilities for our research.
Discover our recent findings and publications: Research.
At the Cell Migration Lab, we develop, co-develop and contribute (in various ways) to various innovative software projects to analyze microscopy images. Below are some of the key software projects we have been involved with:
- SReD: Structural Repetition Detector Plugin for ImageJ and Fiji GitHub
- PhotoFiTT (2024): Phototoxicity Fitness Time Trial. A Quantitative Framework for Assessing Phototoxicity in Live-Cell. GitHub
- CellTracksColab (2024): A platform for compiling, analyzing, and exploring tracking data. GitHub
- NanoPyx (2024): A library for analyzing light microscopy and super-resolution data, successor to NanoJ. GitHub
- DL4MicEverywhere (2024): An extension of ZeroCostDL4Mic featuring interactive Jupyter notebooks for bioimage analysis using deep learning. GitHub
- eSRRF (2023): Provides super-resolution approach and optimal parameter prediction for super-resolution microscopy. GitHub
- Fast4DReg (2023): A Fiji plugin for 2D and 3D video drift correction in all axes. GitHub
- TrackMate (2022): Automated tracking software analyzing bioimages, distributed as a Fiji plugin. GitHub
- ZeroCostDL4Mic (2021): Allows the use of popular deep-learning neural networks for tasks in bioimage analysis. GitHub
- SRRF TFM (2020): Enhances the output of traction force microscopy using super-resolution microscopy. Read More
- FiloMap (2019): ImageJ and R scripts for mapping protein localization within filopodia. GitHub
- FiloQuant (2017): A Fiji plugin for automated detection and quantification of filopodia properties. GitHub
For more details on each project, visit our software page.
At the Cell Migration Lab, we're committed to open science principles. When possible, our extensive research-based datasets are made publicly available to catalyze further scientific discovery and collaboration. These datasets cover diverse topics. Explore our datasets and contribute to the ongoing scientific dialogue. Visit our GitHub page for a comprehensive list of our open datasets.
If you are interested in our deep learning models and training datasets, check our Model Zoo.
At the Cell Migration Lab, we also provide training in several topics in image analysis including tools developed in the lab. Here are some trainings provided:
Workshop in I2K 2024 "Object Tracking and Track Analysis using TrackMate and CellTracksColab
Bioimage Analysis for Quantitative Microscopy course
Motivated students are always welcome to contact us!
Visit our website for more insights, or follow us on Bluesky.
For software, dataset queries, or collaborations, please feel free to contact us.