Title: DeepLineage
Duration: 2022 – 2025
Research Area: Life Science and Medicine
The project “DeepLineage” helps understanding how animals generate and regenerate cell diversity integrating live imaging, lineage tracking, deep learning and big image visualization.
We develop image analysis software for the exploration of cell-tracking data using unsupervised machine learning methods.
In-vivo fluorescence microscopy in the key technology for understanding how tissues form. Modern microscopes deliver staggering amounts of imaging data. Analysing this data is challenging, in particular when the data exceeds the capacity of local random access memory (RAM).
In this project, we contribute to the open-source project mastodon [1], a Fiji-plugin for tracking cells in big imaging data.
Our contribution in the project “DeepLineage” will enable biologists to explore their data interactively using unsupervised machine learning techniques.
Lead