Artificial Intelligence

Mission

Our Artificial Intelligence Group aims to move towards personalized healthcare for patients treated with radiotherapy using deep learning methods.

Members

Alana
Hossein
Marie
Milosh
Luca
Sébastien
Yujing
Zacharie

Projects

AI-based treatment planning for brachytherapy applications

Treatment plan optimization is a routine part of both external beam radiotherapy (EBRT) and high dose rate brachytherapy. High dose rate brachytherapy is a mode of internal radiotherapy in which a source of radiation is placed inside the tumor through several hollow needles known as catheters. For every patient, the process of treatment plan optimization ensures that the dose to the tumor region is sufficient while the dose to the surrounding organs at risk is minimal. Therefore, treatment plan optimization is a key process in ensuring positive radiotherapy outcome.
In high dose rate brachytherapy, treatment planning is a labor-intensive and time-consuming task. The treatment planning cannot begin before the patient is put under anesthesia and the catheters are inserted. This makes the process painful for the patient and costly for the hospital. In addition, the current optimization method only controls the amount of time for which the source dwells at a specific location inside a catheter. However, the number and the location of the catheters are not optimized. 
Hossein investigates the possibility of optimizing the catheter positions and the dwell times prior to catheter insertion using reinforcement learning. In this way, quality of the treatment will improve because the catheters will be inserted according to the optimized plan. 

Treatment Outcome Prediction

Combining radiomics and deep learning we are developing a fully automated treatment outcome prediction model that uses multimodal medical image data and helps physicians to make patient-specific treatment selections for cancer patients receiving radiation treatment.

Deep learning-based brachytherapy catheter reconstruction for breast cancer patients

Brachytherapy is a form of radiotherapy where a sealed radiation source is placed inside or next to the area requiring treatment. There are different types of brachytherapy, my project involves interstitial brachytherapy for treatment of breast cancer. The treatment starts by a radiation oncologist inserting many catheters (needles) inside the breast of the patient aiming to place them inside and around the tumor. Once this is done, a CT scan of the area is acquired followed by a medical physicist manually reconstructing the catheters in 3D. This means that the process is slow and prone to errors. The goal of my project is to automate this whole process using a deep learning algorithm.

Prediction of Radiation Induced Toxicity in High Dose Rate Brachytherapy Treatment of Breast Cancer

The aim of Marie’s current project is to develop a deep learning-based algorithm that can predict recurrence and potential long-term side effects based on a variety of patient-specific parameters for breast cancer patients treated with high dose rate brachytherapy.

McMedHacks

McMedHacks is an 8-week program about deep learning and medical image analysis and accumulates in a hackathon, where participants can solve real-life clinical medical image analysis challenges. The program consists of weekly presentations and workshops that are led by students as well as leaders in medical image analysis from academia and industry. Our mission is to bridge domains and bring deep learning in to the clinic by making it accessible to anyone.

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Publications

2021

Weishaupt, Luca L.

T I Gurman Prize in Physics award

2021.

Abstract | Links | BibTeX

Weishaupt, Luca L.; Thibodeau-Antonacci, Alana; Garant, Aurelie; Singh, Kelita; Miller, Corey; Vuong, Té; Enger, Shirin A.

Deep learning based tumor segmentation of endoscopy images for rectal cancer patients Presentation

ESTRO Annual meeting, 27.08.2021.

Abstract | Links | BibTeX

Thibodeau-Antonacci, Alana; Jafarzadeh, Hossein; Carroll, Liam; Weishaupt, Luca L.

Mitacs Globalink Research Award award

2021.

Abstract | Links | BibTeX

Weishaupt, Luca L.; Thibodeau-Antonacci, Alana; Garant, Aurelie; Singh, Kelita; Miller, Corey; Vuong, Té; Enger, Shirin A.

Inter-Observer Variability and Deep Learning in Rectal Tumor Segmentation from Endoscopy Images Presentation

The COMP Annual Scientific Meeting 2021, 22.06.2021.

Abstract | BibTeX

Weishaupt, Luca L.; Torres, Jose; Camilleri-Broët, Sophie; Rayes, Roni F.; Spicer, Jonathan D.; Maldonado, Sabrina Côté; Enger, Shirin A.

Deep learning-based tumor segmentation on digital images of histopathology slides for microdosimetry applications Journal Article

In: arXiv:2105.01824 [physics], 2021, (arXiv: 2105.01824).

Abstract | Links | BibTeX

Zou, Yujing; Lecavalier-Barsoum, Magali; Enger, Shirin A.

Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images Presentation

CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop, 10.02.2021.

Abstract | BibTeX

Weishaupt, Luca L.

Fire-Up - Radiation Treatment Outcome Prediction Presentation

Fire-Up Presentation, 09.02.2021.

BibTeX

Deufel, Christopher; Weishaupt, Luca L.; Sayed, Hisham Kamal; Choo, Chunhee; Stish, Bradley

Deep learning for automated applicator reconstruction in high-dose-rate prostate brachytherapy Journal Article

In: World Congress of Brachytherapy 2021, 2021, (Type: Journal Article).

Links | BibTeX

Weishaupt, Luca L.; Sayed, Hisham Kamal; Mao, Ximeng; Choo, Chunhee; Stish, Bradley; Enger, Shirin A.; Deufel, Christopher

Approaching automated applicator digitization from a new angle - Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy Journal Article

In: ESTRO 2021, 2021, (Type: Journal Article).

Links | BibTeX

Weishaupt, Luca L.; Sayed, Hisham Kamal; Mao, Ximeng; Choo, Chunhee; Stish, Bradley; Enger, Shirin A.; Deufel, Christopher

Approaching automated applicator digitization from a new angle: using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy Journal Article

In: 2021 ABS Annual Meeting, 2021, (Type: Journal Article).

BibTeX

2020

Weishaupt, Luca L.

Math And Physics Class Of 1965 Prize award

2020.

Abstract | BibTeX

Weishaupt, Luca L.; Torres, Jose; Camilleri-Broët, Sophie; Maldonado, Sabrina Côté; Enger, Shirin A.

Classification and Segmentation of Tumor Cells and Nuclei On Biopsy Slides Using Deep Learning for Microdosimetry Applications Journal Article

In: 2020 Joint AAPM textbar COMP Virtual Meeting, 2020, (Type: Journal Article).

Abstract | Links | BibTeX