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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 426-429, 2022 07.
Article in English | MEDLINE | ID: mdl-36085862

ABSTRACT

Imaging mass cytometry (IMC) is a new advancement in tissue imaging that is quickly gaining wider usage since its recent launch. It improves upon current tissue imaging methods by allowing for a significantly higher number of proteins to be imaged at once on a single tissue slide. For most analyses of IMC data, determining the phenotype of each cell is a crucial step. Current methods of phenotyping require sufficient biological knowledge regarding the protein expression profile of the various cell types. Here, we develop a deep convolutional autoencoder-classifier to automate the cell phenotyping process into four basic cell types. Biopsy tissue from bladder cancer patients is used to evaluate the efficacy of the classification. The model is evaluated and validated through feature importance, confirming that the significant features are biologically relevant. Our results demonstrate the potential of deep learning to automate the task of cell phenotyping for high-dimensional IMC data.


Subject(s)
Image Cytometry , Urinary Bladder Neoplasms , Biopsy , Humans , Immunologic Tests , Phenotype , Urinary Bladder Neoplasms/diagnostic imaging
2.
Cytometry A ; 101(5): 423-433, 2022 05.
Article in English | MEDLINE | ID: mdl-35060322

ABSTRACT

Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available 3D Slicer software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.


Subject(s)
COVID-19 , Data Analysis , Humans , Image Cytometry/methods , Image Processing, Computer-Assisted/methods , Reproducibility of Results , Software
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