Presentation av examensarbete "Domain Adaptation for Combined CT and CBCT Deep Learning Segmentation"
Kontakt: magnus [dot] oskarsson [at] math [dot] lth [dot] se
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Jonas Berg (F16) presenterar sitt examensarbete Domain Adaptation for Combined CT and CBCT Deep Learning Segmentation (Svensk titel: Domänanpassning för Kombinerad CT och CBCT Djupinlärnings-segmentering)
Computed tomography (CT) segmentation models are frequently used within radiotherapy treatment planning, but similar models are not available to the related imaging modality cone beam computed tomography (CBCT) due to the scarcity of labeled data from this domain. Such models could have multiple clinical applications whereby it is of interest to study whether the CT segmentation models can be adapted to generalize to the CBCT domain. This thesis applies multiple different domain adaptation techniques to the male pelvic segmentation problem and compares the relative performance of the models for both CT and CBCT segmentation. The results indicate that all domain adaptation techniques yield large improvements compared to the baseline results and deformable image registration (DIR), but that a novel data augmentation pipeline suggested in this work might be the most efficient route to solving the domain shift problem. This data augmentation pipeline notably improves CBCT dice similarity coefficient (DSC) scores for bladder to 0.900 (baseline 0.553) and for rectum to 0.850 (baseline 0.605). The CBCT results obtained for this method are comparable to the baseline performance on the CT domain, indicating that the data augmentation approach comes close to completely solving the studied domain shift problem.
Magnus Oskarsson (Matematikcentrum)
Pontus Giselsson (Reglerteknik)
Jonas Söderberg (RaySearch)
Niels Christian Overgaard