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Anton Finnson: Clinical dose feature extraction for prediction of dose mimicking parameters

MSc Thesis Presentation

Time: Tue 2021-06-15 10.00 - 10.45

Location: Zoom, meeting ID: 621 4469 8204

Respondent: Anton Finnson


Treating cancer with radiotherapy requires precise planning. Several planning pipelines rely on reference dose mimicking, where one tries to find machine parameters best mimicking a given reference dose. Dose mimicking relies on having a function that quantifies dose similarity well, necessitating methods for feature extraction of dose images. In this thesis we investigate ways of extracting features from clinical dose images, and propose a few proof-of-concept dose mimicking functions using the extracted features. We extend current techniques and lay the foundation for new techniques for feature extraction, using mathematical frameworks developed in entirely different areas. In particular we give an introduction to wavelet theory, which provides signal decomposition techniques suitable for analysing local structure, and propose two different dose mimicking functions using wavelets. Furthermore, we extend ROI-based mimicking functions to use artificial ROIs, and we investigate variational autoencoders and their application to the clinical dose feature extraction problem.

Belongs to: Department of Mathematics
Last changed: Jun 15, 2021