Segmentation
Segmentation was performed using Ilastik and CellProfiler. Ilastik was used to classify pixels into three classes (nuclei, membrane, and background) and to generate probability maps. CellProfiler was used to segment probability maps to generate segmentation masks.
A detailed description on how to combine Ilastik and CellProfiler can be found on the CellProfiler blog.
For further details follow the Imaging Mass Cytometry specific segmentation pipeline developed by Vito Zanotelli: A flexible image segmentation pipeline for heterogneous multiplexed tissue images based on pixel classification
CellProfiler Basic Pipeline
This module can be used to detect cell nuclei
This module can be used to combine multiple membrane markers for improved segmentation of highly multiplexed images. An automatic single cell segmentation on highly multiplexed tissue images was proposed and applied here.
This module can be used to detect cell membrane
This module can be used to detect cytoplasm by subtracting nuclei and membrane mask.
This module extracts the selected mask. In our case, the cell outlines are extracted and converted to an object.
This module converts objects to an image. We use "uint16: Assigns each object a different number, from 1 to 65535 (the numbers that you can put in a 16-bit integer) and numbers all pixels in each object with the object's number. This format can be written out as a .mat or .tiff file if you want to process the label matrix image using another program."
This module saves the image as .tiff or .mat