Fig. 3

A summarized schematic of the segmentation workflow. It consists of three main steps; step 1 produces non-perfect masks for a part of raw data. After visual inspection and pruning, the remaining masks are fed to an initial shallow CNN via weakly supervised training in step 2, just to improve the masking performance. In a multi-stage procedure, these steps are repeated once again by a deeper CNN to follow more abstract features from the inputs to produce the final masks