I am a experimental physicist. In our research, we have our experimental data in a 400*400*400 matrix(x, y, z axis of a 3d space), each entry is associated with a value("brightness"). We expect the brightest entries will form a closed path in the 3d space. But some portion of the path is always too dark to identify, also there are some noise in the space(random bright lines).
Our current algorithm generates random seed points into the space, they are attracted by the brightness of the data. After certain amount of time, they will be trapped in the path. If we record the seed points position we can get the path. Our current algorithm does a fairly good job while I still need to manually add those relatively dark points to the path.
I am not familiar with machine learning, but I am wondering if it can be used on this case? For example, is it possible that I tell the program which data points are on the path, the program will know how to choose the data points for the path in the future. Or if there is some other method we can use? Thanks!