Segmentation of blood stem cells in bright-field images

Background

During the analysis of single-cell time-lapse experiments of proliferating and differentiating blood stem cells, we often have to deal with two types of clumped cells. First, cells that are dividing are inherently clumped for certain time points. Second, due to the rising amount of cells over time one can observe more and more groups of clumped cells, especially in late timepoints. An additional characteristic of our data is that the experiments show cells in all differentiation states with different morphologies.

Goal

In this project, we would like to develop a method that is able to split clumped cells, but also preserves the shape of all cell types.

Data

Time-lapse microscopy data of mouse blood stem cells methods:

Machine learning
Segmentation

Environment

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.

Contact

Diana Mateus

Carsten Marr

ProjectForm
Title:	Segmentation of blood stem cells in bright-field images
Abstract:	During the analysis of single-cell time-lapse experiments of proliferating and differentiating blood stem cells, we often have to deal with two types of clumped cells. First, cells that are dividing are inherently clumped for certain time points. Second, due to the rising amount of cells over time one can observe more and more groups of clumped cells, especially in late timepoints. An additional characteristic of our data is that the experiments show cells in all differentiation states with different morphologies. In this project, we would like to develop a method that is able to split clumped cells, but also preserves the shape of all cell types.
Student:
Director:	Prof. Nassir Navab
Supervisor:	Carsten Marr, Diana Mateus
Type:	DA/MA/BA
Area:
Status:	finished
Start:
Finish:
Thesis (optional):
Picture: