Enpeng Wang

Objective: Accurate preoperative planning for dental implants, especially in edentulous or partially edentulous patients, relies on precise localization of radiographic templates that guide implant positioning. By wearing a patient-specific radiographic template, clinicians can better assess anatomical constraints and plan optimal implant paths. However, due to the low radiopacity of such templates, their spatial position is difficult to determine directly from cone-beam computed tomography (CBCT) scans. Methods: To overcome this limitation, high-resolution optical scans of the templates are acquired, providing detailed geometric information for accurate spatial registration. This paper proposes a geometric-driven cross-modal registration framework that aligns the optical scan model of the radiographic template with patient CBCT data, enhancing registration accuracy through geometric feature extraction such as curvature and occlusal contours. Results: A hybrid deep learning workflow further improves robustness, achieving a root mean square error (RMSE) of 1.68 mm and mean absolute error (MAE) of 1.25 mm. The system also incorporates augmented reality (AR) for real-time surgical navigation. Conclusion: Clinical and phantom experiments validate its effectiveness in supporting precise implant path planning and execution. Significance: Our proposed system enhances the efficiency and safety of dental implant surgery by integrating geometric feature extraction, deep learning-based registration, and AR-assisted navigation.

Self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in various real-world scenarios. Unfortunately, challenges such as specular reflections and soft tissue deformations in endoscopic scenes greatly undermine the performance of these methods, inevitably compromising the accuracy of depth and ego-motion estimation. To address these two problems, we introduce a novel strategy based on image distance transform for robust self-supervised learning for monocular depth estimation, effectively handling specular reflections in endoscopic scenes. Furthermore, we propose a soft tissue deformation constraint based on biomechanical principles, which mitigates the adverse effects of deformed region pixels, ultimately enhancing the model’s depth estimation precision. Additionally, our method employs a lightweight architecture ensuring a reduced number of model parameters and faster inference time. Extensive experiments are conducted on both public datasets (SCARED, SERV-CT) and our own datasets to validate the effectiveness of our method. Compared with other SOTA methods, our approach demonstrates comparable accuracy and robustness while ensuring faster inference time. On the SCARED dataset, our approach attains an RMSE of 4.96 mm with only 2.25M model parameters for depth estimation. Especially, experiment results on SERV-CT dataset and our own datasets further demonstrate the model’s generalization ability and potential clinical value in computer-assisted surgical navigation.

Minimally invasive surgery (MIS) remains technically demanding due to the difficulty of tracking hidden critical structures within the moving anatomy of the patient. In this study, we propose a soft tissue deformation tracking augmented reality (AR) navigation pipeline for laparoscopic surgery of the kidneys. The proposed navigation pipeline addresses two main sub-problems: the initial registration and deformation tracking. Our method utilizes preoperative MR or CT data and binocular laparoscopes without any additional interventional hardware. The initial registration is resolved through a probabilistic rigid registration algorithm and elastic compensation based on dense point cloud reconstruction. For deformation tracking, the sparse feature point displacement vector field continuously provides temporal boundary conditions for the biomechanical model. To enhance the accuracy of the displacement vector field, a novel feature points selection strategy based on deep learning is proposed. Moreover, an ex-vivo experimental method for internal structures error assessment is presented. The ex-vivo experiments indicate an external surface reprojection error of 4.07 ± 2.17mm and a maximum mean absolutely error for internal structures of 2.98mm. In-vivo experiments indicate mean absolutely error of 3.28 ± 0.40mm and 1.90±0.24mm, respectively. The combined qualitative and quantitative findings indicated the potential of our AR-assisted navigation system in improving the clinical application of laparoscopic kidney surgery.

The porous structure of the implant contributes significantly to prosthetic osseointegration in cranio‐maxillofacial defect repair surgeries. This study establishes a system called EasyImplant that can easily and efficiently design customized cranio‐maxillofacial implant with porous structure. Healthy side of the skull model is used to obtain the initial implant model on defective side by mirroring method. According to the curvature of undamaged surface, the initial model can be adjusted and repaired. Based on the initial implant model, porous implant structure can be generated efficiently using create sample points, distance Field and marching Cube algorithms. Finally, the connection plate is obtained through surface extruding and model merging. With EasyImplant, a complete porous implant model can be designed in a short time. The proposed methods can contribute to shorten the period of facial implant designing and reduce the incidence of surgical infection.