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Machine learning for virus-like particle segmentation and classification in electron microscope imaging

de Biolley, Antoine
(2022)

Files

deBiolley_12251600_2022.pdf
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Details

Supervisors
Lee, John Aldo
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil en informatique, à finalité spécialisée
Abstract
The development of virus like particle (VLP) in the pharmaceutical industry is used in the preparation of vaccines that mimic the structure of a virus. Depending on their structure, these multi-protein constructs are categorized into three types: single shell, multi-layer, and coiled ribbon. In the present thesis two machine learning models as well as a standard image processing model have been designed in order to automate the discrimination and counting of the three particles types in a VLP suspension, with an aim at replacing the traditional and time-consuming manual counting of the different particles appearing on electron microscopy images. The performances of each model are analysed and their accuracies are compared.