-
公开(公告)号：US20240386527A1
公开(公告)日：2024-11-21
申请号：US18556098
申请日：2021-05-07
Inventor： Xuming ZHANG , Yancheng LAN
Abstract: A method for establishing a three-dimensional ultrasound image blind denoising model and a use thereof include: adding a speckle noise to three-dimensional biological structure images of a same size and without speckle noise to obtain a training data set; establishing a three-dimensional denoising network based on an encoding-decoding structure, wherein the encoding structure is used to obtain N feature maps of a three-dimensional input image and perform a downsampling to obtain feature maps of different scales; the decoding structure is used to take a feature map obtained by the encoding structure as an input and reconstruct a three-dimensional image without speckle noise through upsampling; dividing the encoding-decoding structure into a plurality of stages by a downsampling structure and an upsampling structure; training the three-dimensional denoising network using the training data set to obtain a three-dimensional ultrasound image blind denoising model.
-
Patent Agency Ranking
2.发明公开公开(公告)号：US20240081648A1
公开(公告)日：2024-03-14
申请号：US18554680
申请日：2021-05-10
Inventor： Xuming ZHANG , Tuo WANG
CPC classification number: A61B5/004 , A61B5/055 , A61B5/7267 , G06N3/084 , G06T7/0012 , G06T2207/30081
Abstract: The present invention discloses a method and a system for prostate multi-modal MR image classification based on a foveated residual network, the method comprising: replacing convolution kernels of a residual network using blur kernels in a foveation operator, thereby constructing a foveated residual network; training the foveated residual network using prostate multi-modal MR images having category labels, to obtain a trained foveated residual network; and classifying, using the foveated residual network, a prostate multi-modal MR image to be classified, so as to obtain a classification result. In the present invention, a foveation operator is designed based on human visual characteristics, blur kernels of the operator are extracted and used to replace convolution kernels in a residual network, thereby constructing a foveated deep learning network which can extract features that conform to the human visual characteristics, thereby improving the classification accuracy of prostate multi-modal MR images.
-
3.发明公开公开(公告)号：US20240257356A1
公开(公告)日：2024-08-01
申请号：US18635023
申请日：2024-04-15
Inventor： Xuming ZHANG , Mingwei WEN , Quan ZHOU
IPC: G06T7/11 , G06T3/4046 , G06T7/00 , G06T7/143 , G16H30/40
CPC classification number: G06T7/11 , G06T3/4046 , G06T7/0012 , G06T7/143 , G16H30/40 , G06T2200/04 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06T2207/30096
Abstract: The disclosure belongs to the field of image segmentation in medical image processing and discloses a three-dimensional medical image segmentation method and system based on short-term and long-term memory self-attention models, in which the method can segment a target area image in the medical image, which includes the following. (1) A training set sample is established. (2) Processing is performed on the original three-dimensional medical image to be segmented to obtain a sample to be segmented. (3) A three-dimensional medical image segmentation network based on short-term and long-term memory self-attention is established and trained. (4) The sample to be segmented is input to the network, and then a segmentation result of the target area in the sample to be segmented is output. By combining CNN and Transformer, a new model for accurate real-time segmentation of the target area (such as a tumor) in the three-dimensional medical image is obtained.
-
4.发明公开公开(公告)号：US20230316549A1
公开(公告)日：2023-10-05
申请号：US17997693
申请日：2021-04-13
Inventor： Xuming ZHANG , Xingxing ZHU
CPC classification number: G06T7/337 , G06T5/20 , G06T7/344 , G06V10/454 , G06V10/761 , G16H30/20 , G06T2207/20081
Abstract: Disclosed are a method for establishing a non-rigid multi-modal medical image registration model and an application thereof, which pertain to the field of medical image registration. The method comprises: establishing a generative adversarial network GAN_dr, wherein a generator G_dr is used to generate a deformation recovered structural representations, and a discriminator D_dr is used to determine whether the structural representations generated by G_dr has effectively recovered deformations; performing calculation with respect to structural representations of a reference image, a floating image, and an actual registered image in each sample in a medical dataset, and using a calculation result to train GAN_dr; establishing a generative adversarial network GAN_ie, wherein a generator G_ie uses the structural representations as an input to estimate a registered image, and a discriminator D_ie is used to determine whether the estimated registered image is consistent with the actual registered image; using the trained G_dr to generate the deformation recovered structural representations corresponding to each sample in the medical dataset, and training GAN_ie; and after connecting the trained G_ie to G_dr, obtaining the registration model. The present invention can achieve fast and accurate matching of medical images.
-
公开(公告)号：US20230196528A1
公开(公告)日：2023-06-22
申请号：US18001295
申请日：2021-01-30
Inventor： Xuming ZHANG , Shaozhuang YE
CPC classification number: G06T5/50 , G06T3/40 , G06T3/60 , G06T5/20 , G06V10/771 , G06V10/806 , G16H30/40 , G06T2207/20081 , G06T2207/20221
Abstract: A multimodal medical image fusion method based on a DARTS network is provided. Feature extraction is performed on a multimodal medical image by using a differentiable architecture search (DARTS) network. The network performs learning by using the gradient of network weight as a loss function in a search phase. A network architecture most suitable for a current dataset is selected from different convolution operations and connections between different nodes, so that features extracted by the network have richer details. In addition, a plurality of indicators that can represent image grayscale information, correlation, detail information, structural features, and image contrast are used as a network loss function, so that the effective fusion of medical images can be implemented in an unsupervised learning way without a gold standard.
-
公开(公告)号：US20190130572A1
公开(公告)日：2019-05-02
申请号：US16094473
申请日：2016-10-09
Inventor： Xuming ZHANG , Fei ZHU , Jingke ZHANG , Jinxia REN , Feng ZHAO , Guanyu LI , Mingyue DING
Abstract: The present invention discloses a registration method and system for a non-rigid multi-modal medical image. The registration method comprises: obtaining local descriptors of a reference image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the reference image; obtaining local descriptors of a floating image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the floating image; and finally obtaining a registration image according to the local descriptors of the reference image and the floating image. In the present, by using self-similarity of the multi-modal medical image and adopting the Zernike moment based local descriptor, the non-rigid multi-modal medical image registration is thus converted into the non-rigid mono-modal medical image registration, thereby greatly improving its accuracy and robustness.
-
-
-
-
-
Search Results
6
records
(took 0.018 seconds)
