This webinar is made up of three presentations:

• AI autocontouring of organs in preclinical radiation studies for cancer

An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research.

Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research. His interests are radiotherapy physics, imaging, preclinical research and Monte Carlo simulations.

• Deep learning prostate segmentation in three-dimensional ultrasound

This presentation will explore the development and validation of a generalisable deep-learning-based automatic prostate segmentation algorithm for three-dimensional ultrasound images. Practical considerations for implementing deep-learning segmentation tools will be explored including the effect of dataset size, image quality, and image type on segmentation performance.

Speaker: Nathan Orlando is a fifth-year PhD candidate in the Department of Medical Biophysics at Western University and Robarts Research Institute in London, Ontario, Canada, supervised by Dr Aaron Fenster and Dr Douglas Hoover. His research has focused on improving ultrasound-guided prostate brachytherapy through both software- and hardware-based solutions. Prior to starting at Western University, Nathan completed a BSc (Hons) in physics at the University of Alberta in Edmonton, Alberta, Canada.

• Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT

In this presentation, we present a deep learning-based method to automatically delineate the swallowing and chewing structures in CT. Its potential for use in radiotherapy treatment planning to improve efficiency is demonstrated through prospective validation.

Speaker: Aditi Iyer, is a senior scientific application developer in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center, where she has served as a key contributor to the open-source Computational Environment for Radiological Research (CERR) software for six years. Her research interests include the application of machine learning and radiomics for image analysis and predictive modeling. Prior to joining MSKCC, she received her master’s degree from Purdue University, Indiana, where she worked on the estimation of multi-subject functional connectivity maps from fMRI data.

Speakers relationship with IOP Publishing

They are accepted authors of the focus collection.

Webinar chairs
Georgios Papanastasiou and Guang Yang, guest editors of the joint Physics in Medicine & Biology and Machine Learning: Science and Technology focus issue, Focus on Machine Learning Models in Medical Imaging.