Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are i...Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are insufficient data for training,the data-driven approach will fail.We propose a feature-based transfer-learning method to extract features from fluoroscopic images.With three subjects and fewer than 100 pairs of real fluoroscopic images,we achieved a mean registration success rate of up to 40%.The proposed method provides a promising solution,using a learning-based registration method when only a limited number of real fluoroscopic images is available.展开更多
Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elasti...Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.展开更多
基金sponsored by the National Natural Science Foundation of China(31771017,31972924,81873997)the Science and Technology Commission of Shanghai Municipality(16441908700)+3 种基金the Innovation Research Plan supported by Shanghai Municipal Education Commission(ZXWF082101)the National Key R&D Program of China(2017YFC0110700,2018YFF0300504,2019YFC0120600)the Natural Science Foundation of Shanghai(18ZR1428600)the Interdisciplinary Program of Shanghai Jiao Tong University(ZH2018QNA06,YG2017MS09).
文摘Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are insufficient data for training,the data-driven approach will fail.We propose a feature-based transfer-learning method to extract features from fluoroscopic images.With three subjects and fewer than 100 pairs of real fluoroscopic images,we achieved a mean registration success rate of up to 40%.The proposed method provides a promising solution,using a learning-based registration method when only a limited number of real fluoroscopic images is available.
基金supported in part by The National Key Research and Development Program of China(2018YFC2001302)the National Natural Science Foundation of China(61976209)+1 种基金CAS International Collaboration Key Project(173211KYSB20190024)Strategic Priority Research Program of CAS(XDB32040000)。
文摘Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.
