interesting....following

Hello,

before I answer, I want to make sure that I have understood the question correctly.

My understanding of the question is that you have MicroCT data sets in which you have marked caries interactively or by image processing. You call this annotation.

In addition, you have 2D radiographs of exactly the same teeth. Are these clinical X-rays or are they in vitro X-rays? I call this dataset in the following clinical/in vitro.

Now you want to transfer the markings from the 3D data set to the 2D data set.

If I had to solve the task, I would make the markings in the MicroCT 3D data using segmentation as a so-called label mask.

Then I would generate a simulated 2D X-ray image from the 3D data set, which is based on the projection of the 2D data set. Do not only use a simple slice, you have to add the projection of all pixels perpenticular to you "projection" and generate an average grayvalue. In ImageJ this would be similar to the algorithm which is used for "z-projection. I call this dataset in the following virtual simulated.

This virtual simulated 2D data set would be generated once with the pure gray scale image data and once using exactly the same parameters with the superimposed label mask.

The two grayscale 2D data sets (virtual simulated and clinical/in vitro) I would match by Image Registration. With the exact same values obtained from the registration, I would transform the virtually simulated 2D data set with Label Mask. Afterwards the Label Mask parts can be overlaid on the clinical/in vitro X-ray data set (alpha channel).

I like to work with open source programs like ImageJ/Fiji. Alternatively you can use Python with for example opencv. You can find good tutorial on Youtube.

Probably you can achieve the same result with any other image processing software.

I just sketched a possible workflow that came to my mind right away. However, the devil is in the details, i.e. it will probably not be as simple as I described it in reality. But it would be boring if there were no more challenges in science.

However, if you want to raise a lot of money for the project due to the current funding policy for grants, then promise to achieve the same result with Artificial Intelligence, deep learning and neural networks. That would be zeitgeist and hip, but it won't work any better than the path I proposed.