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Hi, You could pose this as an inpainting problem, where you train on scans of the full humerus and then do inference on partial images of the humerus with a mask outlining the part you want to extend. Here is a tutorial showing you how to do inpainting. |
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Hello, |
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Hello everyone,
thank you for this interesting project. I have a question.
Currently, I am working on the reconstruction of the humerus from a CT. In several scans, the humerus is not full. I have worked on a statistical model that completes the missing part of the bone. i have the followin quiestion:
can I achieve the same using generative AI?
I have full humeurs scans that I can intentionally remove some images to simulate a missing part of the bone, and a AI model would estimate the missing part?
If yes, my question is:
I have would an input of a number of images (each image is 512 * 512 pixels), but the output of the model would be the same input plus the images generated by the AI? (For example, if I have a case of 200 images but cut it to 150images), the input of the model would be 150, but the ground truth would be 200. How can I manage this difference between input and output?
Can someone guide me on what model I could use?
Is there a tutorial in MONAI that could serve as a reference?
Thanks to all for your help
RIC
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