Publications

Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images
Pedro Vianna
Merve Kulbay
Pamela Boustros
Sara-Ivana Calce
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
Muawiz Chaudary
Samuel Kadoury
Bich Nguyen
Emmanuel Montagnon
Eugene Belilovsky
Guy Wolf
Michael Chassé
An Tang
Guy Cloutier
Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screenin… (voir plus)g and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. Computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.
2023-09-02
IUS (publié)
doi.org
Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images
Pedro Vianna
Merve Kulbay
Pamela Boustros
Sara‐Ivana Calce
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
M.A. Chaudary
Samuel Kadoury
Bich Nguyen
Emmanuel Montagnon
Eugene Belilovsky
Guy Wolf
Michael Chassé
An Tang
Guy Cloutier
Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screenin… (voir plus)g and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. Computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.
2023-09-02
IEEE International Ultrasonics Symposium (publié)
doi.org
122 Is Selenium-75 a Feasible HDR Brachytherapy Source?
Jake Reid
S. Enger
Jonathan Kalinowski
A. Armstrong
John Munro
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
124 Development of a Novel Dosimetry Software for Patient-Specific Intravascular Brachytherapy Treatment Planning on Optical Coherence Tomography Images
Maryam Rahbaran
Jonathan Kalinowski
James Man Git Tsui
Joseph DeCunha
Kevin Croce
Brian Bergmark
Philip Devlin
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
125 Toward the Translation of Rectal Intensity Modulated Brachytherapy for Feasibility and Safety Studies
Jonathan Kalinowski
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
163 Evaluating the Inter-Observer Variability in the Delineation of Rectal Lesions in Endoscopy Images
A. Thibodeau-Antonacci
Corey Miller
L. Weishaupt
Aurélie Garant
T. Vuong
P. Nicolaï
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
181 RapidBrachyTG43: A TG-43 Parameter and Dose Calculation Module for RapidBrachyMCTPs
Jonathan Kalinowski
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
186 Diffusion of 220RN and 212PB in Diffusing Alpha-Emitter Radiation Therapy Dosimetry with Geant4
Victor Daniel Díaz Martínez
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
192 Investigation of the Dose Properties and Source to Source Variabilities in Xoft Source Model S7500
A. Esmaelbeigi
Jonathan Kalinowski
T. Vuong
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
241 Reduction of Metal Artifacts in 7T MRI for Pre-Clinical Diffusing Alpha-Emitting Radiation Therapy Rectal Studies
Mélodie Cyr
Behnaz Behmand
N. Chabaytah
Joud Babik
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
256 Patient-Specific Pre-Treatment Nuclei Size Distribution is of Significance for Post Radiation Therapy Locoregional Recurrence and Survival Outcomes
Yujing Zou
Magali Lecavalier-Barsoum
Manuela Pelmus
Farhad Maleki
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org
259 Development of a Cost-Efficient Scintillation-Fiber Detector for Use in Automated Synthesis of Positron Emission Tomography Radiotracers
Hailey Ahn
Liam Carroll
Robert Hopewell
I-Huang Tsai
S. Enger
2023-08-31
Radiotherapy and Oncology (publié)
doi.org