Multimodal Learning To Improve Segmentation With Intraoperative CBCT & Preoperative CT

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.