*5.3.2.2 Analysis of rough endoplasmic reticulum (RER)*

Quantitative analysis of the surface area covered by the RER cisternae on TEM images can provide valuable insight into functional changes in both acinar and endocrine cells. Quantification of the TEM data, in general, involves manual annotation of structures of interest. This approach proved to be an extremely time-consuming process for quantification of the RER data, as the organelle forms a complex and interconnected network of cisternae. To overcome this issue, Trainable Weka Segmentation (TWS, available as Fiji/ImageJ plugin) provides a machine learning tool capable of automated segmentation [99]. A limited number of manual annotations on a sample (training) TEM image produces a classifier that can be applied to the remaining data to segment images automatically. The following pipeline describes steps for RER analysis:

