Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

Daniela Martina Borgmann; Sandra Mayr; Helene Polin; Lisa Obritzberger; Susanne Schaller; Viktoria Dorfer; Jaroslaw Jacak; Stephan Winkler

doi:10.1007/978-3-319-27340-2_39

Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

Daniela Martina Borgmann
, Sandra Mayr
, Helene Polin
, Lisa Obritzberger
, Susanne Schaller
, Viktoria Dorfer
, Jaroslaw Jacak
, Stephan Winkler

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Editors	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Publisher	Springer
Pages	310-317
Number of pages	8
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_39
Publication status	Published - 2015
Event	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain Duration: 8 Feb 2015 → 13 Feb 2015 http://eurocast2015.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9520
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Country/Territory	Spain
City	Las Palmas, Gran Canaria
Period	08.02.2015 → 13.02.2015
Internet address	http://eurocast2015.fulp.ulpgc.es/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Fluorescence microscopy
Image analysis
Machine learning

Access to Document

10.1007/978-3-319-27340-2_39

Cite this

Borgmann, D. M., Mayr, S., Polin, H., Obritzberger, L., Schaller, S., Dorfer, V., Jacak, J., & Winkler, S. (2015). Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. In F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 310-317). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_39

Borgmann, Daniela Martina ; Mayr, Sandra ; Polin, Helene et al. / Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. pp. 310-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{fa52fc53984a4ae9b4af420c14b71480,

title = "Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images",

abstract = "In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.",

keywords = "Bioinformatics, Fluorescence microscopy, Image analysis, Machine learning",

author = "Borgmann, \{Daniela Martina\} and Sandra Mayr and Helene Polin and Lisa Obritzberger and Susanne Schaller and Viktoria Dorfer and Jaroslaw Jacak and Stephan Winkler",

note = "Funding Information: The work described in this paper has been done within the FFG FIT-IT project NanoDetect: A Bioinformatics Image Processing Framework for Automated Analysis of Cellular Macro and Nano Structures (project number 835918) sponsored by the Austrian Research Promotion Agency. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 15th International Conference on Computer Aided Systems Theory, Eurocast 2015 ; Conference date: 08-02-2015 Through 13-02-2015",

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doi = "10.1007/978-3-319-27340-2\_39",

language = "English",

isbn = "9783319273396",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "310--317",

editor = "Franz Pichler and Roberto Moreno-D{\'i}az and Alexis Quesada-Arencibia",

booktitle = "Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers",

address = "Germany",

url = "http://eurocast2015.fulp.ulpgc.es/",

}

Borgmann, DM, Mayr, S, Polin, H, Obritzberger, L, Schaller, S , Dorfer, V , Jacak, J & Winkler, S 2015, Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9520, Springer, pp. 310-317, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spain, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_39

Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. / Borgmann, Daniela Martina; Mayr, Sandra; Polin, Helene et al.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. p. 310-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

AU - Borgmann, Daniela Martina

AU - Mayr, Sandra

AU - Polin, Helene

AU - Obritzberger, Lisa

AU - Schaller, Susanne

AU - Dorfer, Viktoria

AU - Jacak, Jaroslaw

AU - Winkler, Stephan

N1 - Funding Information: The work described in this paper has been done within the FFG FIT-IT project NanoDetect: A Bioinformatics Image Processing Framework for Automated Analysis of Cellular Macro and Nano Structures (project number 835918) sponsored by the Austrian Research Promotion Agency. Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.

AB - In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.

KW - Bioinformatics

KW - Fluorescence microscopy

KW - Image analysis

KW - Machine learning

UR - https://www.scopus.com/pages/publications/84952308417

U2 - 10.1007/978-3-319-27340-2_39

DO - 10.1007/978-3-319-27340-2_39

M3 - Conference contribution

SN - 9783319273396

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 310

EP - 317

BT - Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers

A2 - Pichler, Franz

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

PB - Springer

T2 - 15th International Conference on Computer Aided Systems Theory, Eurocast 2015

Y2 - 8 February 2015 through 13 February 2015

ER -

Borgmann DM, Mayr S, Polin H, Obritzberger L, Schaller S , Dorfer V et al. Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. In Pichler F, Moreno-Díaz R, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. p. 310-317. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_39

Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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