Cell Segmentation, Tracking and Classification in 3D+t Microscopy Images
Motivation
Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this project, we study mitotic progression automatically using deep learning. This involves identifying and tracking individual cells from 2D and 3D time-lapse microscopy videos of cells. Once the cells are tracked we aim to automatically identify the cell-cycle stages of the individual cells.
Scientific Questions
We develop algorithms for the following scientific questions in this project:
- How to reliably detect, segment and track fluorescently labeled cells in large-scale time-resolved 2D and 3D microscopy images with limited training data?
- How to automatically classify individual cell-cycle stages of dividing cells in 2D+t and 3D+t microscopy images using deep learning methods?
- How to combine the developed approaches to a single pipeline that can be made available to the community via interactive graphical user interfaces?
Theses
New theses are regularly advertised in the area of Segmentation and Tracking of Microscopic Images using Deep Learning. In line with the project description given above, there are several sub problems in this project, which could be interesting topics for Bachelor’s and Master’s theses and could be discussed in a personal meeting.
Partners
- Dr. Daniel Moreno-Andrés, Institute of Biochemistry and Molecular Cell Biology, RWTH Aachen University Hospital
- Univ.-Prof. Dr. Wolfram Antonin, Institute of Biochemistry and Molecular Cell Biology, RWTH Aachen University Hospital
