Portrait of Hossein Jafarzadeh is unavailable

Hossein Jafarzadeh

hossein.jafarzadeh@mila.quebec
PhD - McGill University
Supervisor
Shirin A. Enger
Research Topics
Computer Vision
Deep Learning
Optimization
Reinforcement Learning

Publications

Automatic catheter digitization in breast brachytherapy
Sébastien Quetin
Hossein Jafarzadeh
Jonathan Kalinowski
Hamed A. Bekerat
Boris Bahoric
Farhad Maleki
Shirin Abbasinejad Enger
BACKGROUND: High dose rate (HDR) brachytherapy requires clinicians to digitize catheters manually. This process is time-consuming, complex, … (see more)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: 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.
2025-09-11
Medical Physics (published)
doi.org
A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer.
Björn Morén
Hossein Jafarzadeh
S. Enger
2025-03-25
Comput. Biol. Medicine (published)
doi.org
Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization
Hossein Jafarzadeh
Majd Antaki
Ximeng Mao
Marie Duclos
Farhard Maleki
Shirin A Enger
Objective. Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objecti… (see more)ve, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach. The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results. MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance. MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner’s experience and reduce dose to the organs at risk.
2024-05-20
Physics in Medicine & Biology (published)
doi.org