ArticleScientific reports2019
UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Phagophores originate from endoplasmic reticulum membranes in vasopressin neurons in a mouse model of familial neurohypophysial diabetes insipidus.Cell and tissue research · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- Short-range human cortico-cortical white matter fibers have thinner axons and are less myelinated compared to long-range fibers despite a similar g-ratio.PLoS biology · 2025Article
- Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging.Communications medicine · 2025Article
- Three-dimensional ultrastructural analysis of human skin with the arrector pili muscle interacting with the hair follicle epithelium.Scientific reports · 2025Article
- Photomarking Relocalization Technique for Correlated Two-Photon and Electron Microscopy Imaging of Single Stimulated Synapses.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Morphomics via next-generation electron microscopy.Journal of molecular cell biology · 2024Review
- Ultrastructure of the lamprey head mesoderm reveals evolution of the vertebrate head.iScience · 2023Article
- Unusual nuclear structures in male meiocytes of wild-type rye as revealed by volume microscopy.Annals of botany · 2023Article
- Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks.Scientific reports · 2023Article
- [A region-level contrastive learning-based deep model for glomerular ultrastructure segmentation on electron microscope images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.bioRxiv : the preprint server for biology · 2023Article
- mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.Frontiers in neural circuits · 2023Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- Seasonal Arctic sea ice forecasting with probabilistic deep learning.Nature communications · 2021Article
- Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells.Journal of imaging · 2021Article
- DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation.PLoS computational biology · 2021Article
- Three-Dimensional Structure of Dendritic Spines Revealed by Volume Electron Microscopy Techniques.Frontiers in neuroanatomy · 2021Article
- Generative and discriminative model-based approaches to microscopic image restoration and segmentation.Microscopy (Oxford, England) · 2020Review
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.
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