ArticlePloS one2015
A context-aware delayed agglomeration framework for electron microscopy segmentation.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A ComprehensiveACS energy letters · 2024Review
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- Automated 3D Axonal Morphometry of White Matter.Scientific reports · 2019Article
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- DVID: Distributed Versioned Image-Oriented Dataservice.Frontiers in neural circuits · 2019Article
- A resource for theeLife · 2018Article
- A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring.Sensors (Basel, Switzerland) · 2018Article
- Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila.Nature neuroscience · 2018Article
- Analyzing Image Segmentation for Connectomics.Frontiers in neural circuits · 2018Article
- Article
- Article
- FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms.PLoS computational biology · 2017Article
- Synaptic circuits and their variations within different columns in the visual system of Drosophila.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a "delayed" scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets.
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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.