Multimodal Learning To Improve Segmentation With Intraoperative CBCT & Preoperative CT

arXiv:2406.11650v1 Announce Type: cross
Abstract: 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 …

abstract arxiv availability computer cs.cv cs.lg eess.iv image imaging important improvements medical medical imaging multimodal multimodal learning potential quality scans segmentation tool type visual while