Segmentation of embryonic stem cells in fluorescent images
Embryonic stem cells are a fascinating model system with high biomedical potential. The maintenance of pluripotency is governed by a couple of important transcription factors. To understand the underlying regulatory dynamics, we quantify nuclear protein expression over time from single-cell time-lapsemicroscopy data.
In this project, we want to improve segmentation of cellular nuclei and quantification of nuclear fluorescent signals with machine learning methods. To this end, we will integrate additional information, like cell cycle information, and biological knowledge on typical nuclear shapes.
Tracked single-cell time-lapse microscopy data of mouse embryonic stem cells
- Machine learning
You will have the opportunity to develop your project within a very interdisciplinary team, as the project will be jointly developed at the computational modeling in biology (CMB)
institute at Helmholtz Zentrum and our Chair CAMP/TUM.