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Speaking as a physician who works with both interventional and diagnostic radiology -- the self promotion and style of the post is offputting to say the least. In addition, I'd never, ever, not in a million years, consider any interventional or diagnostic strategy from a blog post, most of which is written in a language, any language, I can not readily understand. Not to mention, I'd like to see names, not 1337 h4xx0r handles ("coolwulf" is great if you're 12 and playing WoW as a Worgen Deathknight, it's not cool if you'd like to convince me to add more tools to my workflow), when I consider modalities. As a sidenote: modalities like this one are common in modern radio diagnostics. Harvard, MIT, Cambridge with King's, Paris Cité, and a few more are working on evidence based (and Open Source/Open Algorithm) approaches to AI diagnostics, all of which seem to have their ups and downs in outcomes. All their services are HIPAA compliant and certified as such, run in-house, do not require me to upload vast amounts of radiographic data to a website operated in a country I wouldn't trust with my daily egg consumption stats. We're not talking funsies at the 7/11 here. We're talking diagnostic and therapeutic decisions. Basing those on a black box "pinky promise, it works" approach, is pretty much how people get killed. Not to mention, it's a 1980s view of things, the "if I can see it, I can cure it" approach. Modern oncology means to diagnose based on genetic and sequential markers, develop individualized strategies, long before imaging modalities become important. And if they do, there's plenty of hard- and software out there to make our lives so insanely easy, why send our stuff to a graphics card in China? |
The radiation oncologist will outline the tumour and a few organs at risk manually. This segmentation algorithm would then steps in and outlines organs that the doctor would not have traditionally contoured. For a lung lesion the RO may contour the lesion and the heart but might not contour both lungs and the diaphragm.
We can then input these segmentations into a treatment planning optimization algorithm that sets the radiation beam angles and collimation to meet constraints that minimize organ dose and maximize tumour dose. So in effect the application of this sort of segmentation is to give more information to an optimizer.
Not that it doesn't have its problems! But I think it's important to note that the application is not diagnostic