ArticleFrontiers in neuroinformatics2014
Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Nanoscale Imaging of Biological Tissues: Techniques, Challenges and Emerging Frontiers.Nanomaterials (Basel, Switzerland) · 2025Review
- DeepACSON automated segmentation of white matter in 3D electron microscopy.Communications biology · 2021Article
- Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.GigaScience · 2020Article
- Article
- 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
- Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy.Current opinion in neurobiology · 2019Review
- Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome.Frontiers in bioengineering and biotechnology · 2019Review
- DVID: Distributed Versioned Image-Oriented Dataservice.Frontiers in neural circuits · 2019Article
- High-precision automated reconstruction of neurons with flood-filling networks.Nature methods · 2018Article
- Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.Frontiers in computational neuroscience · 2017Article
- Article
- Focused ion beams in biology.Nature methods · 2015Review
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.
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