Monthly Archives: January 2022

Opportunity for AI Autosegmentation Vendors

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Over the past four years, I have had the pleasure to get to know and collaborate with TROG, an organization based in Australia and New Zealand that conducts clinical trial research involving radiotherapy. TROG have used ProKnow software to run plan studies for SRS brain treatment as well as SBRT spine, pancreas, and liver cases. They have also done contouring workshops across multiple body sites.

For the 2022 TROG meeting –  and in conjunction with the annual ASMIRT meeting – TROG is taking it up a notch! For one, they are planning an exciting and important treatment plan study using an unprecedented experimental design that will focus on optimizing a plan based on lung function. Also, and as the main topic of this post, they will be doing a very interesting contouring workshop.

The 2022 contouring workshop is particularly exciting to me because it will do the following: (1) explore method(s) to build consensus across a group of expert physicians, (2) measure and visualize the variation in contouring across a large population of professional treatment planners and anatomists, (3) study population consensus vs. expert consensus, and (4) collect results and measure the accuracy of artificial intelligence (AI) based auto-segmentation engines vs. the gold standard and population on the whole.

TROG is inviting all AI/auto-segmentation vendors to participate. This is a great way to test your engines and showcase your results. Whether you are an established vendor, startup company with works in progress, or an academic software research group, I encourage you to participate. You can contact TROG’s Radiation Therapy Manager Alisha Moore ( to get involved.

This is not my project specifically (other than helping them with design and implementation), but I think studies like this are of utmost importance so I wanted to help TROG cast a wide net and maximize involvement. There’s nothing to lose and everything to gain. After all, we cannot prove (or improve) what we do not measure!