Sébastien Quetin

Ph.D. Student
Biological & Biomedical Engineering

Artificial Intelligence Group

Bio

Sébastien was born and raised in France. He studied at INSA Toulouse, a prestigious French Grande École, where he earned a Master’s degree in Applied Mathematics. Passionate about deep learning and its applications in imaging, he is now a PhD student in the Enger Lab at McGill University, developing AI-driven solutions to automate the brachytherapy workflow.

Overall PhD project

Brachytherapy is a form of radiotherapy in which a sealed radiation source is placed inside or near the tumor. The treatment process begins with a radiation oncologist inserting catheters or an applicator into the patient’s body to position them within and around the clinical treatment volume. A CT scan of the area is then acquired, and a medical physicist manually reconstructs the catheters or applicator on the scan. Meanwhile, an oncologist contours the tumor volume and surrounding organs at risk. Finally, a treatment plan is created and optimized to maximize tumor irradiation while minimizing exposure to healthy tissues.
Sébastien’s PhD focuses on automating these tasks to improve the efficiency and speed of brachytherapy treatment. This proof-of-concept pipeline is being developed specifically for breast cancer patients.

Current Projects

AI-based dosimetry pipeline for brachytherapy application

Sébastien is currently developing an automated pipeline that can:

Reconstruct catheters from patient scans, enabling the creation of dwell positions for the radioactive source and treatment planning.
Contour organs at risk and tumors, facilitating treatment plan evaluation.
Predict dose distribution, ensuring accurate assessment of radiation exposure.

This automation aims to streamline the brachytherapy workflow, improving both precision and clinical efficiency.

Past Project

In radiation therapy, the patient’s body is often approximated as water to simplify and accelerate dose calculations. While this approach allows for quick estimations, it lacks accuracy. The gold standard for dose evaluation—Monte Carlo simulations—provides highly precise results by accounting for different tissue properties, but they are too time-consuming for routine clinical use.

To address this, Sébastien trained an AI model capable of generating Monte Carlo-like dose maps in just a few seconds, significantly improving both speed and accuracy in dose assessment.

2025

Quetin, Sébastien; Jafarzadeh, Hossein; Kalinowski, Jonathan; Bekerat, Hamed; Bahoric, Boris; Maleki, Farhad; Enger, Shirin A.

Automatic catheter digitization in breast brachytherapy Journal Article

In: Medical Physics, vol. 52, iss. 9, no. e18107, 2025, ISSN: 2473-4209.

Abstract | Links | BibTeX

@article{nokey,

title = {Automatic catheter digitization in breast brachytherapy},

author = {Sébastien Quetin and Hossein Jafarzadeh and Jonathan Kalinowski and Hamed Bekerat and Boris Bahoric and Farhad Maleki and Shirin A. Enger},

url = {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18107},

doi = {https://doi.org/10.1002/mp.18107},

issn = {2473-4209},

year = {2025},

date = {2025-09-12},

urldate = {2025-09-12},

journal = {Medical Physics},

volume = {52},

number = {e18107},

issue = {9},

abstract = {Background:

High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.

Purpose:

This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.

Methods:

Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.

Results:

Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.

Conclusions:

Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Background:
High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, and depends heavily on clinical experience-especially in breast cancer cases, where catheters may be inserted at varying angles and orientations due to an irregular anatomy.

Purpose:
This study is the first to automate catheter digitization specifically for breast HDR brachytherapy, emphasizing the unique challenges associated with this treatment site. It also introduces a pipeline that automatically digitizes catheters, generates dwell positions, and calculates the delivered dose for new breast cancer patients.

Methods:
Treatment data from 117 breast cancer patients treated with HDR brachytherapy were used. Pseudo-contours for the catheters were created from the treatment digitization points and divided into three classes: catheter body, catheter head, and catheter tip. An nnU-Net pipeline was trained to segment the pseudo-contours on treatment planning computed tomography images of 88 patients (training and validation). Then, pseudo-contours were digitized by separating the catheters into connected components. Predicted catheters with an unusual volume were flagged for manual review. A custom algorithm was designed to report and separate connected components containing colliding catheters. Finally, a spline was fitted to every separated catheter, and the tip was identified on the spline using the tip contour prediction. Dwell positions were placed from the created tip at a regular step size extracted from the DICOM plan file. Distance from each dwell position used during the clinical treatment to the fitted spline (shaft distance) was computed, as well as the distance from the treatment tip to the one identified by our pipeline. Dwell times from the clinical plan were assigned to the nearest generated dwell positions. TG-43 dose in water was computed analytically, and the absorbed dose in the medium was predicted using a published AI-based dose prediction model. Dosimetric comparison between the clinically delivered plan dose and the created automated plan dose was evaluated regarding dosimetric indices percent error.

Results:
Our pipeline was used to digitize 408 catheters on a test set of 29 patients. Shaft distance was on average 0.70 ± 3.91 mm and distance to the tip was on average 1.37 ± 5.25 mm. The dosimetric error between the manual and automated treatment plans was, on average, below 3% for planning target volume V100, V150, V200 and for the lung, heart, skin, and chest wall D2cc and D1cc, in both water and heterogeneous media. For D0.1cc values in all the organs at risk, the average error remained below 5%. The pipeline execution time, including auto-contouring, digitization, and dose to medium prediction, averages 118 s, ranging from 63 to 294 s. The pipeline successfully flagged all cases where digitization was not performed correctly.

Conclusions:
Our pipeline is the first to automate the digitization of catheters for breast brachytherapy, as well as the first to generate dwell positions and predict corresponding AI-based absorbed dose to medium based on automatically digitized catheters. The automatically digitized catheters are in excellent agreement with the manually digitized ones while more accurately reflecting their true anatomical shape.