To analyse the large amounts of image data that are generated by biologists with modern microscopes, machine learning algorithms became increasingly popular.
In collaboration with Frank Schnorrer and Cl ́ement Rodier at Institut de Biologie du Developpement de Marseille, as well as Ian Estabrook at Physics of Life, TU Dresden, this thesis applies the supervised machine learning algorithms ‘Support Vector Machine’ and ‘Random Forest’ to data obtained from fluorescence microscope images of myofibrillogenesis in Drosophila pupae with the aim to identify sarcomeres, the structures that makeup the highly regular myofibrils.
For the implementation in MATLAB, methods such as ‘feature engineering’ are used to increase the performance by reinterpreting the input data and using physical characteristics of the sample system. The project also identifies the problem of class imbalance between positive and negative examples in the input data and counters it with a redefined learning cost. In conclusion, the use of machine learning algorithms for image analysis in biophysics is a very promising way to reduce manual labour. The choice of the best learning algorithm depends on the purpose the obtained output data should serve.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:91380 |
| Date | 16 May 2024 |
| Creators | Sbosny, Leon |
| Contributors | Friedrich, Benjamin M., Timme, Marc, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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