Bone segmentation and extrapolation in Cone-Beam Computed Tomography

Sammanfattning: This work was done within the French R&D center of GE Medical Systems and focused on two main tasks: skull bone segmentation on 3D Cone-Beam Computed Tomography (CBCT) data and skull volumetric shape extrapolation on 3D CBCT data using deep learning approaches. The motivation behind the first task is that it would allow interventional radiologists to visualize only the vessels directly without adding workflow to their procedures and exposing the patients to extra radiations. The motivation behind the second task is that it would help understand and eventually correct some artifacts related to partial volumes. The skull segmentation labels were prepared while taking into ac- count imaging-modality related considerations and anatomy-related considerations. The architecture that was chosen for the segmentation task was chosen after experimenting with three different networks, the hyperparameters were also optimized. The second task explored the feasability of extrapolating the volumetric shape of the skull outside of the field of view with limited data. At first, a simple convolutional autoencoder architecture was explored, then, adversarial training was added. Adversarial training did not improve the performances considerably.