Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community.Abbreviations: AB, autophagic body; AvP, average precision; GUI, graphical user interface; IoU, intersection over union; MVB, multivesicular body; ROI, region of interest; TEM, transmission electron microscopy; WT,wild type.
Keywords: Automated labeling; autophagy; computer vision; electron microscopy; image analysis; machine learning.