About the Project
Contouring tumour volumes is an important early step in the radiotherapy planning process. Several imaging modalities (CT, PET and MRI) are used to guide contouring, but inter-observer variation in contouring tumour volumes can be significant. Deep Learning (DL) methods have been developed for delineation for limited tumour types and have shown good agreement with manual contouring, with a reduction in observer variability. However, combining different complementary imaging modalities (e.g. metabolic information from PET and anatomical information from CT) raises questions about the contribution of each modality to the auto-contouring process. Furthermore, medical images have limited resolution and are often marred by undesirable noise, reducing the information that can be reliably extracted from them. Advances in both the incorporation of physics information (e.g. image acquisition or image reconstruction) into DL algorithms and in the use of hybrid DL methods have been shown to greatly enhance the information that can be derived from medical images. In addition, the inclusion of clinical metadata in algorithm development can contribute to precision.
The aims of this project are:
- to understand the contribution of different complementary imaging modalities to the accuracy of tumour segmentation and to develop algorithms that utilise information from all modalities and,
- to develop new approaches that can integrate complementary information from clinical imaging by using physics-informed or biologically-inspired DL.
The initial focus of the project will be on head and neck and lung cancers. Archived PET-CT and MRI scans of patients who underwent radiotherapy and where the tumour volume has been defined by expert(s) will be used for neural network development. Questions that will be addressed include how to minimise the number of false positive predictions, and how to strengthen the generalisability of developed models.
Funding Note
The deadline for applications for competition funded scholarships on the DPhil in Oncology at University of Oxford has now passed. Applications which were received before noon on Friday 9th December are currently being considered for places on the course (both funded and unfunded). Projects which are filled from this first round of applications will be removed from the website, this will happen in early February. The next application deadline will be at 12.00pm (noon) on Wednesday 1st March. This is for unfunded places only, meaning that, if accepted, you will need to provide your own funding to cover tuition fees and living expenses, either by self-funding or via an external scholarship.
References
1) Trimpl, M.J., Boukerroui, D., Stride, E.P., Vallis, K.A. and Gooding, M.J., 2021. Interactive contouring through contextual deep learning. Medical Physics, 48(6), pp.2951-2959.
2) Bourigault, E., McGowan, D.R., Mehranian, A. and Papież, B.W., 2021, September. Multimodal PET/CT tumour segmentation and prediction of progression-free survival using a full-scale UNet with attention. In 3D Head and Neck Tumor Segmentation in PET/CT Challenge (pp. 189-201). Springer, Cham.
3) Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S. and Yang, L., 2021. Physics-informed machine learning. Nature Reviews Physics, 3(6), pp.422-440.
4) Maier, A., Köstler, H., Heisig, M., Krauss, P. and Yang, S.H., 2022. Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future. Progress in Biomedical Engineering.
5) Trimpl, M.J., Primakov, S., Lambin, P., Stride, E.P., Vallis, K.A. and Gooding, M.J., 2022. Beyond automatic medical image segmentation—the spectrum between fully manual and fully automatic delineation. Physics in Medicine & Biology, 67(12), p.12TR01.
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