Deep Learning Aided Radiation Therapy Planning in Glioblastoma Patients (2021 - )
Glioblastoma is the deadliest type of brain cancer. The goal of this project is to help clinicians treat Glioblastoma patients in a way that not only maximizes their survival time, but also improves their quality of life.
To treat Glioblastoma patients, a tri-modal therapy including surgery, Radiation Therapy (RT) and chemotherapy is prescribed. The effectiveness of the surgery and RT is heavily dependent on the analysis and quality of the obtained medical images. By improving Computer-Assisted Interventions (CAI) for image guided RT in brain tumor patients, we aim to improve the effectiveness of RT in Glioblastoma.
To enable CAI using Deep Learning (DL) methods, a large amount of labelled data is necessary. These labels can only be generated by medical experts and are not only time consuming and expensive to generate, but they can also be subjective or even erroneous. Using Self-Supervised Learning (SSL) methods, we use the intrinsic information of medical images as artificial labels to train large DL methods. This enables us to train on a large amount of data and mitigate and explore potential biases of the model. Based on the pre-trained SSL models, we will automate the time consuming and error prone tasks of Target Volume (TV) and the Organ At Risk (OAR) segmentation. Additionally, we will build an interpretable DL model to predict the settings of the RT and the absorbed radiation dose. This model will enable clinicians to experiment with a wide range of RT settings to better personalize the treatment for a given patient.
Peer-reviewed Publications (journal or conference)
- F. W. Ten, D. Yuan, N. Jabareen, Y. J. Phua, R. Eils, S. Lukassen, and C. Conrad (2023). resVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles. Frontiers in Cell and Developmental Biology, 11, 104.
- N. Jabareen and S. Lukassen (2022). Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet Architecture. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_32
Other (presentations at conferences or preprints)