Lung cancer is the leading cause of cancer deaths worldwide. A potential early indicator of lung cancer is the presence of lung nodules that can be detected through screening. Open thoracotomy, a surgical approach for nodule resection, carries inherent risk which can be minimized with use of Video-Assisted Thoracoscopic Surgery (VATS); a minimally invasive alternative, reducing risks and recovery time. Precise nodule localization is crucial for efficient navigation during VATS. The utilization of intraoperative Cone-Beam Computed Tomography (CBCT), an imaging modality, can improve localization of the nodules. However, this poses a challenge when attempting to accurately align the nodule position from the CBCT to the surgical view. To address this, we propose a novel approach that segments corresponding features visible in both modalities, specifically the rib cages and Alexis O Wound Protector/Retractor (Alexis). The segmentation of these features is performed using YOLOv8 allowing image registration and alignment of the CBCT data with the surgical view. With the established correspondence, we can gauge possible camera locations and create an augmented reality overlay of the surgical site to provide real-time guidance in VATS.