Zaineb Amor
Bone segmentation and extrapolation in Cone-Beam Computed Tomography
Abstract
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 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 in-
terventional 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 even-
tually correct some artifacts related to partial volumes. The skull segmenta-
tion labels were prepared while taking into account imaging-modality related
considerations and anatomy-related considerations. The archiecture 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 out-
side 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.
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