Intraoperative medical imaging, particularly Cone-beam computed tomography
(CBCT), is an important tool facilitating computer aided interventions, despite
a lower visual quality. While this 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 (affine and elastic transformations
facilitating misalignment) on the final segmentation performance. As an
application scenario, we focus on the segmentation of liver and liver tumor
semantic segmentation and evaluate the effect of intraoperative image quality
and misalignment on segmentation performance. To accomplish this, high quality,
labelled CTs are defined as preoperative and used as a basis to simulate
intraoperative CBCT. We show that the fusion of preoperative CT and simulated,
intraoperative CBCT mostly improves segmentation performance and that even
clearly misaligned preoperative data has the potential to improve segmentation
performance.

通过融合预操作 CT 和模拟的术中 CBCT，研究了术中图像质量和错位对分割性能的影响，结果显示融合图像能够改善分割性能，即使显著错位的预操作数据也有潜力提高分割性能。