Finding Golgi Stacks in Electron Micrographs

Neil Fordyce; Stephen McKenna; Christian Hacker; John Lucocq

Finding Golgi Stacks in Electron Micrographs

Neil Fordyce
, Stephen McKenna
, Christian Hacker
, John Lucocq

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.

Original language	English
Title of host publication	Medical Image Understanding and Analysis
Subtitle of host publication	MIUA
Editors	Constantino Reyes-Aldasoro, Greg Slabaugh
Place of Publication	United Kingdom
Publisher	British Machine Vision Association and Society for Pattern Recognition
Pages	59-64
Number of pages	6
ISBN (Print)	1901725510
Publication status	Published - Jul 2014
Event	18th Annual Conference in Medical Image Understanding and Analysis - Moore Complex, Royal Holloway, London, United Kingdom Duration: 9 Jul 2014 → 11 Jul 2014 http://www.city.ac.uk/medical-image-understanding-and-analysis-2014

Conference

Conference	18th Annual Conference in Medical Image Understanding and Analysis
Abbreviated title	MIUA 2014
Country/Territory	United Kingdom
City	London
Period	9/07/14 → 11/07/14
Internet address	http://www.city.ac.uk/medical-image-understanding-and-analysis-2014

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http://www.city.ac.uk/medical-image-understanding-and-analysis-2014/proceedings

Cite this

@inproceedings{d7dc9b3680cf49388976c2ce49104d0b,

title = "Finding Golgi Stacks in Electron Micrographs",

abstract = "Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.",

author = "Neil Fordyce and Stephen McKenna and Christian Hacker and John Lucocq",

year = "2014",

month = jul,

language = "English",

isbn = "1901725510",

pages = "59--64",

editor = "Constantino Reyes-Aldasoro and Greg Slabaugh",

booktitle = "Medical Image Understanding and Analysis",

publisher = "British Machine Vision Association and Society for Pattern Recognition",

note = "18th Annual Conference in Medical Image Understanding and Analysis , MIUA 2014 ; Conference date: 09-07-2014 Through 11-07-2014",

url = "http://www.city.ac.uk/medical-image-understanding-and-analysis-2014",

}

Fordyce, N, McKenna, S, Hacker, C & Lucocq, J 2014, Finding Golgi Stacks in Electron Micrographs. in C Reyes-Aldasoro & G Slabaugh (eds), Medical Image Understanding and Analysis: MIUA. British Machine Vision Association and Society for Pattern Recognition, United Kingdom, pp. 59-64, 18th Annual Conference in Medical Image Understanding and Analysis , London, United Kingdom, 9/07/14. <http://www.city.ac.uk/medical-image-understanding-and-analysis-2014/proceedings>

Finding Golgi Stacks in Electron Micrographs. / Fordyce, Neil; McKenna, Stephen; Hacker, Christian et al.
Medical Image Understanding and Analysis: MIUA. ed. / Constantino Reyes-Aldasoro; Greg Slabaugh. United Kingdom: British Machine Vision Association and Society for Pattern Recognition, 2014. p. 59-64.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Finding Golgi Stacks in Electron Micrographs

AU - Fordyce, Neil

AU - McKenna, Stephen

AU - Hacker, Christian

AU - Lucocq, John

PY - 2014/7

Y1 - 2014/7

N2 - Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.

AB - Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.

M3 - Conference contribution

SN - 1901725510

SP - 59

EP - 64

BT - Medical Image Understanding and Analysis

A2 - Reyes-Aldasoro, Constantino

A2 - Slabaugh, Greg

PB - British Machine Vision Association and Society for Pattern Recognition

CY - United Kingdom

T2 - 18th Annual Conference in Medical Image Understanding and Analysis

Y2 - 9 July 2014 through 11 July 2014

ER -

Finding Golgi Stacks in Electron Micrographs

Abstract

Conference

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