This sandwich placement will be based in the BRF In Vivo Imaging STP supervised by Jan-Bas Prins and Thomas Snoeks.
The BRF has two project options for the 2024 - 2025 sandwich placement year. Only one sandwich placement will be offered depending on applications received and outcome of interviews.
Project background and description
Project 1: Development of image analysis pipelines using deep learning and image analysis techniques
Within the In Vivo Imaging (IVI) unit, we have a number of scanners that can image inside the body and quantify how anatomical changes with, for example, time .
These images help to understand, for example, if a tumour is responding to anti-cancer treatment. At the moment, this response to treatment is quantified manually by drawing the tumour in the images before/after treatment to quantify volume. This can be a slow and laborious process and may be prone to bias.
We want to automate the manual segmentation process. The primary aim of this project is to take our database of liver cancer images and use image analysis software packages to develop a semi-automated method of quantifying these tumours. This involves working with software packages, including machine learning techniques (for example ), and learning basic coding techniques. We will then investigate whether we can adapt the pipeline for other IVI projects.
The new techniques can be made freely available to all Crick researchers and, if appropriate, outside of the Crick. These techniques could reduce bias in quantifying changes in anatomy, as well as provide a very significant time saving to researchers at the Crick and beyond.
Project 2: Development of multi-modality animal scanner beds
Animal models are essential when studying complex disease processes or intricate aspects of healthy biology that cannot be investigated in other ways. For example, understanding multifactorial processes such as the role of the immune system in disease or investigating the efficacy of cancer treatment and disease progression.
At the In-vivo Imaging facility, researchers utilize various imaging techniques to visualize disease and biological processes in animals over time. These techniques include Magnetic Resonance Imaging (MRI), ultrasound imaging, optical imaging (fluorescence and bioluminescence), X-ray Computed Tomography (CT), and nuclear imaging. Additionally, the facility performs image-guided radiation therapy on mice, similar to how it is done in cancer patients. During imaging procedures on mice, general anaesthesia is used to minimize stress and prevent movement during the scans. Specialized beds with anaesthesia delivery systems are used. The beds also have physiological monitoring (e.g. respiration) and maintain the animal (e.g. temperature). The design of these animal beds vary in overall shape and connections depending on the imaging equipment manufacturer. For certain research questions, it may be necessary to use different scanners on the same animal, such as performing both a CT scan and an MRI scan or using an MRI scan for treatment planning and radiation therapy. In such cases, it is crucial to transfer the animal between scanners without changing its position. This requires the development of animal beds that can fit into two or more imaging systems [1, 2].
The aim of this project is to design, prototype, and test multimodality beds. The candidate will gain knowledge in various imaging modalities, scanners, technical design, and 3D printing. An ideal candidate would have an engineering background and a strong interest in medical imaging and biomedical sciences.
The post holder should embody and demonstrate the Crick ethos and ways of working: bold, open and collegial. The Candidate must be registered at a UK Higher Education Institution, studying in the UK and must have completed a minimum of two years’ undergraduate study in a relevant discipline, and on track to receive a final degree grade of 2:1 or 1. In addition, they should be able demonstrate the following experience and key competencies:
- Project 1 would suit a student with basic programming skills. A background in mathematics, physics, computing, or biomedical sciences is desirable.
- Project 2 would suit candidate would have an engineering background and a strong interest in medical imaging and biomedical sciences
- Good knowledge in relevant scientific area(s)
- Good written and spoken communication skills
- Ability to work independently and also capable of interacting within a group
1. Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall, L.O., . . . Silbiger, M.L. (1995)
MRI segmentation: methods and applications.
Magnetic Resonance Imaging 13: 343-368. PubMed abstract
2. Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J. and Maier-Hein, K.H. (2021)
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.
Nature Methods 18: 203-211. PubMed abstract
1. Corroyer-Dulmont, A., Falzone, N., Kersemans, V., Thompson, J., Hill, M., Allen, P.D., . . . Vallis, K.A. (2017)
MRI-guided radiotherapy of the SK-N-SH neuroblastoma xenograft model using a small animal radiation research platform.
British Journal of Radiology 90: 20160427. PubMed abstract
2. Kersemans, V., Beech, J.S., Gilchrist, S., Kinchesh, P., Allen, P.D., Thompson, J., . . . Smart, S.C. (2017)
An efficient and robust MRI-guided radiotherapy planning approach for targeting abdominal organs and tumours in the mouse.
PLOS ONE 12: e0176693. PubMed abstract