Hi @tom
Many thanks for replying.

So you have an array 512,512,30 points and labels of x,y,z which are the centers of the objects of interest? You don’t have label information about the “radius”/Variance but want to extract that from the image?

Yes, that is correct. I have a 3D image of size height and width of 512 and having a depth of 30. The bright objects of interest are are all of the same size. All I have is their x,y,z location in a csv file. I cant use points as inputs to my model so thats why I figured one way to solve this would be to create a gaussian at the x,y,z point and then use torch.max() between a zero tensor and my gaussian tensor. This would give me a label or target tensor.

How many points do you have 1, 10, 100? What is the range? Are all brightnesses the same? Or in other words if we consider superposition of gaussians, would they all have the same coefficient? Is it homogenous in the coordinates (i.e. circles rather than ellipses)?

Per volume image I have about 50 points in the csv file. I am cropping a 32*32*30 block around these points in the dataloader. Yes, they all have the same brightness and they all have the same shape (circles).
Given, they all have the same variance maybe scatter+convolution would work, as you mentioned, but I’m not familiar with how to achieve this.
Many Thanks