Prediction of stem cell differentiation in human amniotic membrane images using machine learning

Lisa Obritzberger; Daniela Borgmann; Susanne Schaller; Viktoria Dorfer; Andrea Lindenmair; Susanne Wolbank; Simone Hennerbichler; Heinz Redl; Stephan Winkler

doi:10.1007/978-3-319-27340-2_40

Prediction of stem cell differentiation in human amniotic membrane images using machine learning

Lisa Obritzberger, Daniela Borgmann, Susanne Schaller, Viktoria Dorfer, Andrea Lindenmair, Susanne Wolbank, Simone Hennerbichler, Heinz Redl, Stephan Winkler

Research Center Hagenberg

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

Abstract

It has been shown that it is possible to differentiate viable amniotic membrane towards osteogenic lineage, i.e. bony tissue. This process of mineralization may take several weeks and can show different manifestations per sample. The tissue can only be used, when the mineralization process is advanced in a certain degree. Therefore, a forecast of the development of mineralization would be helpful to save time and resources. This paper shows how a prediction on the development of mineralization can be made by using several image processing techniques, machine learning methods, and hybrid ensembles of machine learning algorithms.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Redakteure/-innen	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Herausgeber (Verlag)	Springer
Seiten	318-325
Seitenumfang	8
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_40
Publikationsstatus	Veröffentlicht - 2015
Veranstaltung	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spanien Dauer: 8 Feb. 2015 → 13 Feb. 2015 http://eurocast2015.fulp.ulpgc.es/

Publikationsreihe

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

Konferenz

Konferenz	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Land/Gebiet	Spanien
Ort	Las Palmas, Gran Canaria
Zeitraum	08.02.2015 → 13.02.2015
Internetadresse	http://eurocast2015.fulp.ulpgc.es/

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10.1007/978-3-319-27340-2_40

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Obritzberger, L., Borgmann, D., Schaller, S., Dorfer, V., Lindenmair, A., Wolbank, S., Hennerbichler, S., Redl, H., & Winkler, S. (2015). Prediction of stem cell differentiation in human amniotic membrane images using machine learning. in F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Hrsg.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (S. 318-325). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_40

Obritzberger, Lisa ; Borgmann, Daniela ; Schaller, Susanne et al. / Prediction of stem cell differentiation in human amniotic membrane images using machine learning. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. S. 318-325 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f77a2b8893694c3884712b7a4c9714d3,

title = "Prediction of stem cell differentiation in human amniotic membrane images using machine learning",

abstract = "It has been shown that it is possible to differentiate viable amniotic membrane towards osteogenic lineage, i.e. bony tissue. This process of mineralization may take several weeks and can show different manifestations per sample. The tissue can only be used, when the mineralization process is advanced in a certain degree. Therefore, a forecast of the development of mineralization would be helpful to save time and resources. This paper shows how a prediction on the development of mineralization can be made by using several image processing techniques, machine learning methods, and hybrid ensembles of machine learning algorithms.",

keywords = "Hybride machine learning ensembles, Image processing, Osteogenic tissue engineering",

author = "Lisa Obritzberger and Daniela Borgmann and Susanne Schaller and Viktoria Dorfer and Andrea Lindenmair and Susanne Wolbank and Simone Hennerbichler and Heinz Redl and Stephan Winkler",

note = "Funding Information: The work described in this paper was done within the FIT-IT project number 835918 NanoDetect sponsored by the Austrian Research Promotion Agency (FFG). 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",

year = "2015",

doi = "10.1007/978-3-319-27340-2_40",

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 = "318--325",

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/",

}

Obritzberger, L, Borgmann, D, Schaller, S , Dorfer, V, Lindenmair, A, Wolbank, S, Hennerbichler, S, Redl, H & Winkler, S 2015, Prediction of stem cell differentiation in human amniotic membrane images using machine learning. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (Hrsg.), 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), Bd. 9520, Springer, S. 318-325, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spanien, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_40

Prediction of stem cell differentiation in human amniotic membrane images using machine learning. / Obritzberger, Lisa; Borgmann, Daniela; Schaller, Susanne et al.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. S. 318-325 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Prediction of stem cell differentiation in human amniotic membrane images using machine learning

AU - Obritzberger, Lisa

AU - Borgmann, Daniela

AU - Schaller, Susanne

AU - Dorfer, Viktoria

AU - Lindenmair, Andrea

AU - Wolbank, Susanne

AU - Hennerbichler, Simone

AU - Redl, Heinz

AU - Winkler, Stephan

N1 - Funding Information: The work described in this paper was done within the FIT-IT project number 835918 NanoDetect sponsored by the Austrian Research Promotion Agency (FFG). Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - It has been shown that it is possible to differentiate viable amniotic membrane towards osteogenic lineage, i.e. bony tissue. This process of mineralization may take several weeks and can show different manifestations per sample. The tissue can only be used, when the mineralization process is advanced in a certain degree. Therefore, a forecast of the development of mineralization would be helpful to save time and resources. This paper shows how a prediction on the development of mineralization can be made by using several image processing techniques, machine learning methods, and hybrid ensembles of machine learning algorithms.

AB - It has been shown that it is possible to differentiate viable amniotic membrane towards osteogenic lineage, i.e. bony tissue. This process of mineralization may take several weeks and can show different manifestations per sample. The tissue can only be used, when the mineralization process is advanced in a certain degree. Therefore, a forecast of the development of mineralization would be helpful to save time and resources. This paper shows how a prediction on the development of mineralization can be made by using several image processing techniques, machine learning methods, and hybrid ensembles of machine learning algorithms.

KW - Hybride machine learning ensembles

KW - Image processing

KW - Osteogenic tissue engineering

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U2 - 10.1007/978-3-319-27340-2_40

DO - 10.1007/978-3-319-27340-2_40

M3 - Conference contribution

AN - SCOPUS:84952304093

SN - 9783319273396

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

SP - 318

EP - 325

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 -

Obritzberger L, Borgmann D, Schaller S , Dorfer V, Lindenmair A, Wolbank S et al. Prediction of stem cell differentiation in human amniotic membrane images using machine learning. in Pichler F, Moreno-Díaz R, Quesada-Arencibia A, Hrsg., Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. S. 318-325. (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_40

Prediction of stem cell differentiation in human amniotic membrane images using machine learning

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