Protocols

GALA specifications

Information


Unique identifier OMICS_19727
Name GALA
Alternative name Graph-based Active Learning of Agglomeration
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Stability Stable
Maintained Yes

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Versioning


No version available

Maintainers


  • person_outline Juan Nunez-Iglesias
  • person_outline Juan Nunez-Iglesias

Additional information


GALA was developed in the FlyEM project context.

Publications for Graph-based Active Learning of Agglomeration

GALA citations

 (2)
library_books

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode Guided Agglomeration

2017
Front Comput Neurosci
PMCID: 5660712
PMID: 29114215
DOI: 10.3389/fncom.2017.00097

[…] We first performed over-segmentation of the volume by applying a watershed transform to the BPM from the ConvNet and then the resulting segments were merged using the graph-based active learning of agglomeration (GALA) algorithm (Nunez-Iglesias et al., , ), an agglomeration algorithm based on supervised learning of a policy that determines whether each pair of adja […]

call_split

An automated images to graphs framework for high resolution connectomics

2015
Front Neuroinform
PMCID: 4534860
PMID: 26321942
DOI: 10.3389/fninf.2015.00020
call_split See protocol

[…] into a wiring diagram of the brain. To assemble this pipeline, we begin with membrane detection (Ciresan et al., ), and then create three-dimensional neuron segments, using Rhoana (Kaynig et al., ), Gala (Nunez-Iglesias et al., ), or a watershed-based approach. These are the nodes in our graph, and are compared using the Adjusted Rand index, computed by comparing to neuroanatomist created ground […]

GALA institution(s)
FlyEM Project, HHMI, Ashburn, VA, USA; Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Video Computing Group, Department of Electrical Engineering, University of California at Riverside, Riverside, CA, USA
GALA funding source(s)
Supported by the Howard Hughes Medical Institute and the Victorian Life Sciences Computation Initiative.

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