Geometric Domain Adaptation for CBCT Segmentation

In this project, we perform a computational domain transfer to introduce cone-beam artifacts to the training data. We evaluate its impact on the results of supervised training for the segmentation of the lungs. For this, already labeled CT volumes are reconstructed to artificial CBCT volumes without a complex deep learning-based method, like introduced by Jia X et al.,5 but rather by computational reconstruction. The purpose is to have a network for stable segmentation on real CBCT volumes. A major advantage of our approach is that the artificial
CBCT volumes can not only be computed easily from thoracic CT volumes but also the pixel-wise segmentation can be re-used without putting in the great effort of labeling. This allows for supervised training.