ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology

ERnet is a deep learning-based software tool for automatic segmentation and classification of structures in the endoplasmic reticulum. ERnet is compatible with many fluorescence imaging modalities and can uncover subtle phenotypic changes. The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

Automated summary

ERnet is a deep learning-based software tool that automatically segments and classifies structures in the endoplasmic reticulum, allowing for the quantification of structural changes and the identification of subtle phenotypic differences. It utilizes state-of-the-art semantic segmentation methods and connectivity graphs to accurately and efficiently quantify the connectivity and integrity of ER networks. ERnet has been validated using data from various imaging methods and can be deployed in a high-throughput and unbiased manner to inform on disease progression and response to therapy.