ArticleJournal of neuroscience methods2014
A modular hierarchical approach to 3D electron microscopy image segmentation.
Article in Journal of neuroscience methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 69 citations in OpenAlex.
- Review
- Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.Scientific reports · 2018Article
- A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images.BioData mining · 2018Article
- Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections.Bioinformatics (Oxford, England) · 2017Article
- DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation.Bioinformatics (Oxford, England) · 2017Article
- Cell Detection Using Extremal Regions in a Semisupervised Learning Framework.Journal of healthcare engineering · 2017Article
- Image Segmentation Using Hierarchical Merge Tree.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
- Progress Towards Mammalian Whole-Brain Cellular Connectomics.Frontiers in neuroanatomy · 2016Article
- Focused ion beams in biology.Nature methods · 2015Review
- Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images.Journal of neuroscience methods · 2015Article
- A context-aware delayed agglomeration framework for electron microscopy segmentation.PloS one · 2015Article
- Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.Frontiers in neuroinformatics · 2014Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy.
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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.