Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM)

Stefan Röhrl; Matthias Ugele; Christian Klenk; Dominik Heim; Oliver Hayden; Klaus Diepold

doi:10.1007/978-3-030-45096-0_35

Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM)

Stefan Röhrl, Matthias Ugele, Christian Klenk, Dominik Heim, Oliver Hayden, Klaus Diepold

Chair of Data Processing

Technical University of Munich

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

Abstract

The differentiation and counting of leukocytes is essential for the diagnosis of leukemia. This work investigates the suitability of Deep Convolutional Autoencoders and Principal Component Analysis (PCA) to generate robust features from the 3D image data of a digital holographic microscope (DHM). The results show that the feature space is not trivially separable in both cases. A terminal classification by a Support Vector Machine (SVM) favors the uncorrelated PCA features.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	281-288
Number of pages	8
ISBN (Print)	9783030450953
DOIs	https://doi.org/10.1007/978-3-030-45096-0_35
State	Published - 2020
Event	17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spain Duration: 17 Feb 2019 → 22 Feb 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12014 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	17/02/19 → 22/02/19

Keywords

Autoencoder
Blood cell analysis
Convolutional neural networks
Digital holographic microscopy
Phase images

Access to Document

10.1007/978-3-030-45096-0_35

Cite this

Röhrl, S., Ugele, M., Klenk, C., Heim, D., Hayden, O., & Diepold, K. (2020). Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM). In R. Moreno-Díaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers (pp. 281-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS). Springer. https://doi.org/10.1007/978-3-030-45096-0_35

Röhrl, Stefan ; Ugele, Matthias ; Klenk, Christian et al. / Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM). Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. editor / Roberto Moreno-Díaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2020. pp. 281-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The differentiation and counting of leukocytes is essential for the diagnosis of leukemia. This work investigates the suitability of Deep Convolutional Autoencoders and Principal Component Analysis (PCA) to generate robust features from the 3D image data of a digital holographic microscope (DHM). The results show that the feature space is not trivially separable in both cases. A terminal classification by a Support Vector Machine (SVM) favors the uncorrelated PCA features.",

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author = "Stefan R{\"o}hrl and Matthias Ugele and Christian Klenk and Dominik Heim and Oliver Hayden and Klaus Diepold",

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doi = "10.1007/978-3-030-45096-0_35",

language = "English",

isbn = "9783030450953",

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

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Röhrl, S, Ugele, M, Klenk, C, Heim, D, Hayden, O & Diepold, K 2020, Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM). in R Moreno-Díaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12014 LNCS, Springer, pp. 281-288, 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019, Las Palmas de Gran Canaria, Spain, 17/02/19. https://doi.org/10.1007/978-3-030-45096-0_35

Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM). / Röhrl, Stefan; Ugele, Matthias; Klenk, Christian et al.
Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Díaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2020. p. 281-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12014 LNCS).

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

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AU - Hayden, Oliver

AU - Diepold, Klaus

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AB - The differentiation and counting of leukocytes is essential for the diagnosis of leukemia. This work investigates the suitability of Deep Convolutional Autoencoders and Principal Component Analysis (PCA) to generate robust features from the 3D image data of a digital holographic microscope (DHM). The results show that the feature space is not trivially separable in both cases. A terminal classification by a Support Vector Machine (SVM) favors the uncorrelated PCA features.

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KW - Phase images

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Röhrl S, Ugele M, Klenk C, Heim D, Hayden O , Diepold K. Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM). In Moreno-Díaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Springer. 2020. p. 281-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45096-0_35

Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM)

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Keywords

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