Classification of organelle objects using high resolution imaging and machine learning in 2D and 3D cancer cell systems

Ling Wang; Margarida Barroso; Joshua Goldwag; Cassandra L. Roberge; David T. Corr

doi:10.1117/12.2610343

7 March 2022 Classification of organelle objects using high resolution imaging and machine learning in 2D and 3D cancer cell systems

Ling Wang, Margarida Barroso, Joshua Goldwag, Cassandra L. Roberge, David T. Corr

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Proceedings Volume PC11944, Multiscale Imaging and Spectroscopy III; PC119440C (2022) https://doi.org/10.1117/12.2610343
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Breast cancer is a highly heterogenous disease, both phenotypically and genetically. Here, we propose that the spatial context of organelles, specifically their subcellular location and inter-organelle relationships (topology), can be used to inform breast cancer cell classification. Thus, Organelle-Topology-Cell-Classification-Pipeline (OTCCP) was introduced to quantify the topological features of subcellular organelles, remove the bias of visual interpretation, and classify different breast cancer cell lines using a machine learning method. Our goal is to investigate the role of 3D cancer cell growth on the heterogeneity of organelle topology and morphology to increase the understanding of cancer biology on a subcellular level.

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Ling Wang, Margarida Barroso, Joshua Goldwag, Cassandra L. Roberge, and David T. Corr "Classification of organelle objects using high resolution imaging and machine learning in 2D and 3D cancer cell systems", Proc. SPIE PC11944, Multiscale Imaging and Spectroscopy III, PC119440C (7 March 2022); https://doi.org/10.1117/12.2610343

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KEYWORDS

Imaging systems

Cancer

Classification systems

Machine learning

3D image processing

Breast cancer

Image resolution

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