ArticleNature methods2023
ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- COST1 stimulates RHD3 GTPase activity to maintain ER morphology and plant growth inScience advances · 2026Article
- DeepBranchAI: a transferable 3D segmentation model for branching networks.Frontiers in artificial intelligence · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- nERdy: network analysis of endoplasmic reticulum dynamics.Communications biology · 2025Article
- Single Mitochondrion Morphology-Function Relationship Analysis Using Fluorescent Probes and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Thor: a platform for cell-level investigation of spatial transcriptomics and histology.Nature communications · 2025Article
- Memristor-Based Spiking Neuromorphic Systems Toward Brain-Inspired Perception and Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- SwinCell: a 3D transformer and flow-based framework for improved cell segmentation.Communications biology · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- Nellie: automated organelle segmentation, tracking and hierarchical feature extraction in 2D/3D live-cell microscopy.Nature methods · 2025Article
- Artificial Intelligence for Organelle Segmentation in Live-Cell Imaging.Research (Washington, D.C.) · 2025Review
- Fast and Long-Term Super-Resolution Imaging of Endoplasmic Reticulum Nano-structural Dynamics in Living Cells Using a Neural Network.Small science · 2025Article
- Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity.Bioinformatics (Oxford, England) · 2024Article
- Self-inspired learning for denoising live-cell super-resolution microscopy.Nature methods · 2024Article
- A nanoscale visual exploration of the pathogenic effects of bacterial extracellular vesicles on host cells.Journal of nanobiotechnology · 2024Article
- Impact of photobleaching on quantitative, spatio-temporal, super-resolution imaging of mitochondria in liveNpj imaging · 2024Article
- Quantitation of ER Morphology and Dynamics.Methods in molecular biology (Clifton, N.J.) · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.