Sébastien Quetin

Ph.D. Student
Biological & Biomedical Engineering

Artificial Intelligence Group

Bio

Sébastien was born and raised in France. He attended INSA Toulouse, a five-year French engineering school where he specialized in Mathematics and Data Science. He is passionate about Artificial Intelligence and deep learning. He is thrilled to join Enger Lab at McGill University to apply Artificial Intelligence to subjects that really matter. He will be working on deep learning-based medical image analysis. Apart from science and programming, he loves sports and discovering new things.

Current Projects

AI-based dosimetry for brachytherapy application

Brachytherapy is a form of radiotherapy where a sealed radiation source is placed inside or in close proximity to the tumor. The treatment starts by a radiation oncologist inserting catheters or an applicator inside the patient’s body of the patient aiming to place them inside and around the clinical treatment volume. Once this is done, a CT scan of the area is acquired. The catheters/applicator are manually reconstructed on the CT by a medical physicist. Organs at risk and tumor volumes are contoured by an oncologist. Finally a treatment is created and optimized to irradiate as much as possible the tumor while sparing organs at risk. Irradiations are simulated using Monte-Carlo principle and considering the patient’s body as water.

The main goal of my PhD project is to develop a precise automated dosimetry algorithm that would take into account patient tissues heterogeneity and replace time consuming Monte-Carlo simulations.

Catheter digitization for brachytherapy application

Brachytherapy is a form of radiotherapy where a sealed radiation source is placed inside or in close proximity to the tumor. There are different types of brachytherapy, my project involves interstitial brachytherapy for treatment of breast cancer. The treatment starts by a radiation oncologist inserting catheters inside the breast of the patient aiming to place them inside and around the clinical treatment volume. Once this is done, a CT scan of the area is acquired. The catheters are manually reconstructed on the CT by a medical physicist. Organs at risk and tumor volumes are contoured by an oncologist. Finally the treatment can be planned.

One goal of my PhD project is to automate the process of catheter digitization using a deep learning algorithm to reduced time spent by experienced staff.