Abstract: A method for establishing a non-rigid multi-modal medical image registration model includes establishing a generative adversarial network GAN_dr; 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; using a calculation result to train GAN_dr; establishing a generative adversarial network GAN_ie; using the trained G_dr to generate the deformation recovered structural representations corresponding to each sample in the medical dataset; training GAN_ie; and after connecting the trained G_ie to G_dr, obtaining the registration model.

Abstract: The present invention belongs to the field of medical image registration, and more specifically, relates to a building method of a multi-modal three-dimensional medical image segmentation and registration model, and an application thereof. The method includes: acquiring medical images of two modalities of each target, and respectively using the images as a reference image and a floating image to acquire a training sample; and using the training sample to simultaneously optimize three network parameters, so as to acquire a segmentation and registration model formed by a reference image segmentation model, a floating image segmentation model, and a registration model. The reference image and floating image segmentation models are respectively used to perform multi-scale segmentation on corresponding images to acquire multi-scale segmentation results having the same maximum scale as the original images.

Abstract: Disclosed is a method for establishing a 3D medical image segmentation model based on masked modeling and application thereof includes: establishing a semi-supervised learning network, wherein a student network includes an encoding module for extracting latent features and a segmentation decoder that predicts segmentation results, a teacher network includes an encoding module and a segmentation decoder that are structurally consistent with the student network; training the semi-supervised learning network, wherein during training, two random masking operations are performed on each image, and the image is input to the two networks respectively; optimizing and updating the weight of the student network, and transferring the updated weight to the teacher network, wherein the training loss function includes prototype representation loss, which is used to characterize the difference between the prototypes extracted and generated by the two networks; the student network may further include a reconstruction decoder

Abstract: Disclosed in the present invention is a temporal information enhancement-based method for 3D medical image segmentation, belonging to the field of medical image segmentation. The method provides a circle transformer module for extraction and fusion of temporal information, and uses temporal input to improve the training effect of a deep learning model, thereby effectively eliminating interference of similar features and blurred images. In a training phase, an input sample is a temporal sequence, the model training effect is enhanced by extracting temporal information, and segmentation results before and after the combination of temporal information are both constrained, so that the model is no longer temporally dependent. In comparison with a training method in which a single sample is input, the present invention can improve the accuracy of an encoder-decoder structure-based segmentation model without costs.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract: A preparation method of an ant nest like porous silicon for a lithium-ion battery comprises: (1) enabling a magnesium silicide raw material to react for 2-24 h in an ammonia gas or an atmosphere containing an ammonia gas at 600-900° C. to obtain a crude product containing porous silicon; and (2) subjecting the crude product containing porous silicon to an acid pickling treatment to obtain the ant nest like porous silicon. The preparation method has the advantages of simplicity and easiness. A large amount of porous silicon can be obtained by directly heating the magnesium silicide raw material in the ammonia gas or a mixed gas of the ammonia gas and an inert gas with a high yield.

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.

Abstract: The present invention discloses a preparation method of ant nest like porous silicon for a lithium-ion battery, comprising: (1) enabling a magnesium silicide raw material to react for 2-24 h in an atmosphere containing ammonia gas at 600-900 DEG C. so as to obtain a crude product containing porous silicon (3Mg2Si+4NH3?3Si+2Mg3N2+6H2); the magnesium silicide raw material has a particle size of 0.2-10 ?m; and (2) subjecting the crude product containing porous silicon obtained in the step (1) to acid pickling so as to obtain ant nest like porous silicon for a lithium-ion battery. By improving the overall process flow of the porous silicon key preparation method as well as parameters and conditions of respective reaction steps, compared with the prior art, the preparation method has the advantage of simplicity and easiness, a large amount of porous micron silicon can be obtained by directly heating the obtained magnesium silicide in ammonia gas (or a mixed gas of ammonia gas and inert gas), and the yield is high.

Abstract: Systems and methods configured to produce electrical discharges in compositions, such as those, for example, configured to produce electrical discharges in compositions that comprise mixtures of materials, such as a mixture of a material having a high dielectric constant and a material having a low dielectric constant (e.g., a composition of a liquid having a high dielectric constant and a liquid having a low dielectric constant, a composition of a solid having a high dielectric constant and a liquid having a low dielectric constant, and similar compositions), and further systems and methods configured to produce materials, such as through material modification and/or material synthesis, in part, resulting from producing electrical discharges in compositions.

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.

Abstract: Methods for the reformation of gaseous hydrocarbons are provided. The methods can include forming a bubble containing the gaseous hydrocarbon in a liquid. The bubble can be generated to pass in a gap between a pair of electrodes, whereby an electrical discharge is generated in the bubble at the gap between the electrodes. The electrodes can be a metal or metal alloy with a high melting point so they can sustain high voltages of up to about 200 kilovolts. The gaseous hydrocarbon can be combined with an additive gas such as molecular oxygen or carbon dioxide. The reformation of the gaseous hydrocarbon can produce mixtures containing one or more of H2, CO, H2O, CO2, and a lower hydrocarbon such as ethane or ethylene. The reformation of the gaseous hydrocarbon can produce low amounts of CO2 and H2O, e.g. about 15 mol-% or less.

Abstract: Systems and methods configured to produce electrical discharges in compositions, such as those, for example, configured to produce electrical discharges in compositions that comprise mixtures of materials, such as a mixture of a material having a high dielectric constant and a material having a low dielectric constant (e.g., a composition of a liquid having a high dielectric constant and a liquid having a low dielectric constant, a composition of a solid having a high dielectric constant and a liquid having a low dielectric constant, and similar compositions), and further systems and methods configured to produce materials, such as through material modification and/or material synthesis, in part, resulting from producing electrical discharges in compositions.