Hi, I'm working on a project where I have to segment out nucleus of cells in a volume. I use random_walker with define markers for every nuclei. If I try to run it on the all volume the segmentation works well but it's very slow. So I used util.view_as_blocks function from scikit to split my image and markers and I loop over the different block using joblib: rw_dapi_chunks = Parallel(n_jobs = 4)(delayed(segmentation.random_walker)(chunks[i,j], chunks_markers[i,j], beta = 3000, mode='cg_mg') for i in range(2) for j in range(2)) So I end up with a list of 4 images and if I combine these images (btw I'm not sure what is the best way for doing that? I just add them up in an empty array) everything work well, except that I end-up with nuclei with 2 different labels: [image: Images intégrées 1] Am I doing something wrong? Is there a way to split your images in block for parallel computing but being able to get the labeling right at the end? Best, Cedric