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ArthroNet:a monocular depth estimation technique with 3d segmented maps for knee arthroscopy

智慧医学(英文) 2023年第2期3卷 129-138页

作者： Shahnewaz Ali Ajay K.Pandey School of Electrical Engineering and Robotics Queensland University of TechnologyGardens PointBrisbaneQueensland 4001Austrulia

Background Lack of depth perception from medical imaging systems is one of the long-standing technological limitations of minimally invasive *** ability to visualize anatomical structures in 3d can improve conventional arthroscopic surgeries,as a full 3d semantic representation of the surgical site can directly improve surgeons’*** also brings the possibility of intraoperative image registration with preoperative clinical records for the development of semi-autonomous,and fully autonomous *** study aimed to present a novel monocular depth prediction model to infer depth maps from a single-color arthroscopic video *** We applied a novel technique that provides the ability to combine both supervised and self-supervised loss terms and thus eliminate the drawback of each *** enabled the estimation of edge-preserving depth maps from a single untextured arthroscopic *** proposed image acquisition technique projected artificial textures on the surface to improve the quality of disparity maps from stereo ***,following the integration of the attention-ware multi-scale feature extraction technique along with scene global contextual constraints and multiscale depth fusion,the model could predict reliable and accurate tissue depth of the surgical sites that complies with scene *** A total of 4,128 stereo frames from a knee phantom were used to train a network,and during the pre-trained stage,the network learned disparity maps from the stereo *** fine-tuned training phase uses 12,695 knee arthroscopic stereo frames from cadaver experiments along with their corresponding coarse disparity maps obtained from the stereo matching *** a supervised fashion,the network learns the left image to the disparity map transformation process,whereas the self-supervised loss term refines the coarse depth map by minimizing reprojection,gradients,and structural dissimilarity ***,our method produ

关键词： Monocular depth estimation technique 3d segmented maps Knee arthroscopic

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