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 ± 2.17 mm and a maximum mean absolutely error for internal structures of 2.98 mm. In-vivo experiments indicate mean absolutely error of 3.28 ± 0.40 mm and 1.90 ± 0.24 mm, 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.

Orthodontic treatment is a lengthy process that requires regular in-person dental monitoring, making remote dental monitoring a viable alternative when face-to-face consultation is not possible. In this study, we propose an improved 3D teeth reconstruction framework that automatically restores the shape, arrangement, and dental occlusion of upper and lower teeth from five intra-oral photographs to aid orthodontists in visualizing the condition of patients in virtual consultations. The framework comprises a parametric model that leverages statistical shape modeling to describe the shape and arrangement of teeth, a modified U-net that extracts teeth contours from intra-oral images, and an iterative process that alternates between finding point correspondences and optimizing a compound loss function to fit the parametric teeth model to predicted teeth contours. We perform a five-fold cross-validation on a dataset of 95 orthodontic cases and report an average Chamfer distance of 1.0121$mm^{2}$and an average Dice similarity coefficient of 0.7672 on all the test samples in the cross-validation, demonstrating a significant improvement compared with the previous work. Our teeth reconstruction framework provides a feasible solution for visualizing 3D teeth models in remote orthodontic consultations.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.