  Cone-beam computed tomography (CBCT) is an important tool facilitating
computer aided interventions, despite often suffering from artifacts that pose
challenges for accurate interpretation. While the degraded image quality can
affect downstream segmentation, the availability of high quality, preoperative
scans represents potential for improvements. Here we consider a setting where
preoperative CT and intraoperative CBCT scans are available, however, the
alignment (registration) between the scans is imperfect. We propose a
multimodal learning method that fuses roughly aligned CBCT and CT scans and
investigate the effect of CBCT quality and misalignment on the final
segmentation performance. For that purpose, we make use of a synthetically
generated data set containing real CT and synthetic CBCT volumes. As an
application scenario, we focus on liver and liver tumor segmentation. We show
that the fusion of preoperative CT and simulated, intraoperative CBCT mostly
improves segmentation performance (compared to using intraoperative CBCT only)
and that even clearly misaligned preoperative data has the potential to improve
segmentation performance.
