Accurate automated segmentation of autophagic bodies in yeast vacuoles using cellpose 2.0
Document Type
Article
Publication Date
2024
Department/School
Mathematics
Publication Title
Autophagy
Abstract
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.
Link to Published Version
Recommended Citation
Marron, E. C., Backues, J., Ross, A. M., & Backues, S. K. (2024). Accurate automated segmentation of autophagic bodies in yeast vacuoles using cellpose 2.0. Autophagy, 20(9), 2092–2099. https://doi.org/10.1080/15548627.2024.2353458
Comments
A. M. Ross is a faculty member in EMU's Department of Mathematics and Statistics.
S. K. Backues is a faculty member in EMU's Department of Chemistry.
*E. C. Marron is an EMU student.