Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

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 is to perform automatic segmentation of EM images, followed b...
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Gespeichert in:

Autor*in:	Juan eNunez-Iglesias [verfasserIn] Ryan eKennedy [verfasserIn] Stephen M Plaza [verfasserIn] Anirban eChakraborty [verfasserIn] William T Katz [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	machine learning Electron microscopy image segmentation python software engineering

Übergeordnetes Werk:	In: Frontiers in Neuroinformatics - Frontiers Media S.A., 2008, 8(2014)
Übergeordnetes Werk:	volume:8 ; year:2014

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fninf.2014.00034

Katalog-ID:	DOAJ029384494

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allfieldsSound	10.3389/fninf.2014.00034 doi (DE-627)DOAJ029384494 (DE-599)DOAJ70798fda875c4944ad2a5fc933bfbda7 DE-627 ger DE-627 rakwb eng RC321-571 Juan eNunez-Iglesias verfasserin aut Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We 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 limitations of the gala library and how we intend to address them. machine learning Electron microscopy image segmentation python software engineering Neurosciences. Biological psychiatry. Neuropsychiatry Juan eNunez-Iglesias verfasserin aut Ryan eKennedy verfasserin aut Stephen M Plaza verfasserin aut Anirban eChakraborty verfasserin aut William T Katz verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 8(2014) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:8 year:2014 https://doi.org/10.3389/fninf.2014.00034 kostenfrei https://doaj.org/article/70798fda875c4944ad2a5fc933bfbda7 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00034/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014
language	English
source	In Frontiers in Neuroinformatics 8(2014) volume:8 year:2014
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topic_facet	machine learning Electron microscopy image segmentation python software engineering Neurosciences. Biological psychiatry. Neuropsychiatry
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container_title	Frontiers in Neuroinformatics
authorswithroles_txt_mv	Juan eNunez-Iglesias @@aut@@ Ryan eKennedy @@aut@@ Stephen M Plaza @@aut@@ Anirban eChakraborty @@aut@@ William T Katz @@aut@@
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callnumber-first	R - Medicine
author	Juan eNunez-Iglesias
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topic_title	RC321-571 Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages machine learning Electron microscopy image segmentation python software engineering
topic	misc RC321-571 misc machine learning misc Electron microscopy misc image segmentation misc python misc software engineering misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc machine learning misc Electron microscopy misc image segmentation misc python misc software engineering misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages
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title_auth	Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages
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 is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We 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 limitations of the gala library and how we intend to address them.
abstractGer	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 is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We 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 limitations of the gala library and how we intend to address them.
abstract_unstemmed	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 is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We 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 limitations of the gala library and how we intend to address them.
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title_short	Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages
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Graph-based Active Learning of Agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

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