Development and Application of Novel Methods to Analyze Cells and Cell Lineages in a High Throughput Manner (2018 - )
Many experiments in biology require a large number of samples to allow for conclusive statements. It is thus of utmost importance to reduce the cost per sample as much as possible. This cost can be both in terms of money and time. If a single sample takes multiple hours and one needs hundreds or thousands of samples, the undertaking quickly becomes infeasible. Every significant reduction in manual work can thus make an infeasible project feasible.
In this project we want to study the effect of changes (mutations) in the genome - some of them lethal - on the development of C. elegans embryos and its cell lineage. Yet this requires the tracking of all their cells over time and through cell divisions. While some automatic methods to do this exist, all require several hours of manual curation per sample to get an error-free result.
To overcome this, we are developing new tracking algorithms employing modern machine learning methods applied to volumetric time series data (3d+time).
C. elegans provides us with a prime example. Its development is stereotypical, each wild type (without mutations) organism exhibits the identical number of cells and division pattern. This makes it possible to automatically pin-point both errors in the tracking algorithm and true changes in the development due to mutations.
Analyzing these changes will help us to expand our understanding of the gene regulatory networks induced by the genome, and how they are affected by mutations, a key challenge of developmental biology.
Peer-reviewed Publications (journal or conference)
- 1. A. Krull*, P. Hirsch*, C. Rother, A. Schiffrin, and C. Krull (2020) Artificial-intelligence-driven scanning probe microscopy. (*shared first) Commun Phys 3, 54. https://doi.org/10.1038/s42005-020-0317-3
- P. Hirsch, and D. Kainmueller (2020) An auxiliary task for learning nuclei segmentation in 3D microscopy images.Proceedings of Machine Learning Research 121(304), 318.
- L. Mais*, P. Hirsch*, and D. Kainmueller (2020) PatchPerPix for instance segmentation. (*shared first) In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12370. Springer, Cham. https://doi.org/10.1007/978-3-030-58595-2_18
- J.L. Rumberger, X. Yu, P. Hirsch, M. Dohmen, V.E. Guarino, A. Mokarian, L. Mais, J. Funke, and D. Kainmueller (2021). How Shift equivariance impacts metric learning for instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Other (presentations at conferences or preprints)
- P. Hirsch and D. Kainmueller. An Auxiliary Loss for Learning Nuclei Segmentation in 3D Microscopy Images. Poster presentation at Frontiers in Imaging Science II, Janelia Research Campus, 1-4 May 2019.