This sandwich placement will be based in the Advanced Light Microscopy STP supervised by Kurt Anderson.
Project background and description
Light microscopy is one of the oldest and most powerful methods in biology, enabling researchers to visualise the dynamics of molecules, cells, and tissues. The Crick Advanced Light Microscopy Science Technology Platform supports a wide variety of advanced microscopes which routinely produce terabyte-size datasets. For example, we have two light sheet microscopes, designed to image sensitive multi-cellular samples (such as organoids), or even whole organisms (e.g. developing fish embryos), in three dimensions and multiple colours for up to 24 hours – a single experiment can generate thousands of individual images. Prior to subjecting such datasets to analysis, or even before they can be visualised, some degree of data processing is required1. The aim of this project is to therefore build and optimise custom image data processing pipelines, to be run on our high-performance computing cluster (CAMP), facilitating flexible and reusable big image data processing.
Following this processing stage, it is desirable for these images to be subjected to some form of automated, quantitative analysis. A common first step is the detection and segmentation of cell nuclei, which can be achieved using a wide variety of different methods ranging from conventional approaches to cutting-edge deep learning-based approaches2. A secondary aim of this project will therefore seek to establish a standard, flexible approach to common analysis tasks performed on large image datasets.
Candidate Background
This project would suit a student from a computational discipline (computer science, engineering, mathematics, physics), with some coding experience (in any language) and an interest in learning about biomedical science.
References
1. Royer, L. (2019)
Multi-dimensional microscopy datasets: Storing, processing, and visualizing. iBiology Talks
Available at: https://www.ibiology.org/techniques/multi-dimensional-microscopy-datasets/.
2. Weigert, M., Schmidt, U., Haase, R., Sugawara, K. and Myers, G. (2020)
Star-convex polyhedra for 3D object detection and segmentation in microscopy
Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020: 3655-3662.
