Rotation Invariant Clustering of 3D Cell Nuclei Shapes

Patrick Wagner, Jakob Paul Morath, Arturo Zychlinsky, Klaus Robert Muller, Wojciech Samek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cellular imaging with confocal fluorescence laser microscopy gave rise to many new insights into the cellular machinery. One interesting observation suggests that morphology of cell nucleus plays a key role for neutrophilic function, which is an essential part of the innate immune system of most mammals. Due to the increasing availability of high resolution 3D images coming from the microscope, machine learning becomes a promising tool for automatically discovering underlying hidden structures. Here, the major difficulty consists of selecting an appropriate representation for characterizing the morphology of cell nucleus. In this work we tackle this problem and propose a fully unsupervised mechanism for finding structure in high-throughput 3D image data. The key component of our approach is based on Generic Fourier Transform (GFT) for 2D images, which for 3D involves spherical coordinate transformation prior to fast Discrete Fourier Transformation. On top on GFT we apply dimensionality reduction with Principal Component Analysis, followed by generative cluster analysis with a Gaussian Mixture Model. We validate our new approach first on a synthetic 3D-MNIST dataset with random rotations, where quantitative and qualitative results confirm the applicability of the proposed pipeline for exploring shape space in a purely unsupervised manner. Then we apply our proposed technique to a new collected dataset of high resolution 3D images of neutrophile nuclei suggesting a clustering model with six significant clusters of morphological cell nuclei prototypes. We visualize differences in the cell shape clusters by providing prototypical examples of neutrophilic cell nuclei.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6022-6027
Number of pages6
ISBN (Electronic)9781538613115
DOIs
Publication statusPublished - 2019 Jul
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 2019 Jul 232019 Jul 27

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period19/7/2319/7/27

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Wagner, P., Morath, J. P., Zychlinsky, A., Muller, K. R., & Samek, W. (2019). Rotation Invariant Clustering of 3D Cell Nuclei Shapes. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 6022-6027). [8856734] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8856734