ArticlePloS one2014
Automated detection of synapses in serial section transmission electron microscopy image stacks.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.IEEE transactions on medical imaging · 2024Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- Article
- Automatic classification and neurotransmitter prediction of synapses in electron microscopy.Biological imaging · 2022Article
- Automated Synapse Detection Method for Cerebellar Connectomics.Frontiers in neuroanatomy · 2022Article
- Cellular connectomes as arbiters of local circuit models in the cerebral cortex.Nature communications · 2021Article
- Two Stream Active Query Suggestion for Active Learning in Connectomics.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2020Article
- Multiscale Imaging Approach for Studying the Central Nervous System: Methodology and Perspective.Frontiers in neuroscience · 2020Article
- Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome.Scientific reports · 2019Article
- Reprogramming the brain with synthetic neurobiology.Current opinion in biotechnology · 2019Review
- Learning cellular morphology with neural networks.Nature communications · 2019Article
- Software tools for automated transmission electron microscopy.Nature methods · 2019Review
- Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy.Current opinion in neurobiology · 2019Review
- Effective automated pipeline for 3D reconstruction of synapses based on deep learning.BMC bioinformatics · 2018Article
- Comparison of 3D cellular imaging techniques based on scanned electron probes: Serial block face SEM vs. Axial bright-field STEM tomography.Journal of structural biology · 2018Article
- Fully-Automatic Synapse Prediction and Validation on a Large Data Set.Frontiers in neural circuits · 2018Article
- Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies.Frontiers in neuroanatomy · 2018Article
- A Computational Synaptic Antibody Characterization Tool for Array Tomography.Frontiers in neuroanatomy · 2018Article
- Article
- Quantifying Golgi structure using EM: combining volume-SEM and stereology for higher throughput.Histochemistry and cell biology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).
Indexed as
Identifiers
What OpenQuestion holds
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.