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
A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during ima...A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.展开更多
Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial ne...Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.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National Natural Science Foundation of China(No.60271033)
文摘A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.
文摘Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.
