ArticleMedical image analysis2015
Large-scale automatic reconstruction of neuronal processes from electron microscopy images.
Article in Medical image analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.NeuroImage · 2025Article
- Editorial: Advances in volume electron microscopy for brain imaging: methods, applications, and affordability.Frontiers in neuroscience · 2025Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
- 3D reconstruction of the cerebellar germinal layer reveals tunneling connections between developing granule cells.Science advances · 2023Article
- Volume electron microscopy.Nature reviews. Methods primers · 2022Article
- Modeling Neurons in 3D at the Nanoscale.Advances in experimental medicine and biology · 2022Article
- Emerging electron microscopy and 3D methodologies to interrogate Staphylococcus aureus osteomyelitis in murine models.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2021Article
- Advanced Technologies for Local Neural Circuits in the Cerebral Cortex.Frontiers in neuroanatomy · 2021Article
- A simple and robust method for automating analysis of naïve and regenerating peripheral nerves.PloS one · 2021Article
- Generative and discriminative model-based approaches to microscopic image restoration and segmentation.Microscopy (Oxford, England) · 2020Review
- UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images.Scientific reports · 2019Article
- Developmental Rewiring between Cerebellar Climbing Fibers and Purkinje Cells Begins with Positive Feedback Synapse Addition.Cell reports · 2019Article
- Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI.Brain structure & function · 2019Article
- Automated 3D Axonal Morphometry of White Matter.Scientific reports · 2019Article
- Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy.Current opinion in neurobiology · 2019Review
- Quantification of surviving neurons after contusion, dislocation, and distraction spinal cord injuries using automated methods.Journal of experimental neuroscience · 2019Article
- Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.Scientific reports · 2018Article
- High-precision automated reconstruction of neurons with flood-filling networks.Nature methods · 2018Article
- A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble.Frontiers in neuroscience · 2018Article
- Methods for Mapping Neuronal Activity to Synaptic Connectivity: Lessons From Larval Zebrafish.Frontiers in neural circuits · 2018Article
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Authors and funding
9 authors.
Funding
Abstract
Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a 27,000 μm(3) volume of brain tissue over a cube of 30 μm in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles.
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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.