36 results about "Liver ct" patented technology

The invention relates to the technical field of image enhancement, in particular to a liver CT image enhancement method and system. The method comprises the following steps: acquiring an initial window of each connected domain; obtaining the density change degree of each initial window according to the density distribution of pixel points in each initial window on the CT image; obtaining a multi-scale image corresponding to the CT image, and obtaining the high-frequency information change degree of each initial window according to the frequency domain distribution characteristics of the initial windows corresponding to the same positions among different-scale images; obtaining a window correction coefficient of each initial window, correcting each initial window, and obtaining a correction window of each initial window; and performing adaptive histogram equalization on the images of different scales according to the correction window, and performing weighted fusion in combination with the confusion degree of the images of the corresponding scales to obtain an enhanced CT image. According to the method, the effect of influence enhancement is improved by obtaining the accurate window size during image equalization processing.

The invention discloses a CT image auxiliary detection method and system for liver focal lesions, and the method comprises the steps: receiving and screening multi-dimensional liver CT image data, and generating a standardized DICOM data set based on a preset layer thickness threshold value, a resolution threshold value and an artifact filtering rule; performing two-stage focus positioning analysis on the standardized DICOM data set, and outputting a focus positioning map containing focus coordinates, sizes and average CT values; starting an interactive image optimization engine based on the focus positioning map, responding to a window width and window level adjustment instruction and measurement tool operation of a physician in real time, generating a dynamically enhanced focus visible range and synchronously updating focus parameters; and integrating the visual field data and the clinical rule base to generate a structured report, automatically filling fields seen by the image by checking a focus list, and outputting a diagnosis proposal meeting the DICOM standard in linkage with an image-text report module. By utilizing the embodiment of the invention, standardization and intelligence of auxiliary diagnosis can be realized, and the accuracy and efficiency of liver lesion detection are improved.

The invention provides a fracture repair and topology analysis method for a blood vessel segmentation mask in a liver CT image, and the method specifically comprises the steps: 1) carrying out the skeletonization of the liver blood vessel segmentation mask, and extracting a blood vessel skeleton, a skeleton end point, and a blood vessel radius rough value at a skeleton point; 2) solving a connected blood vessel center line (1) for the purpose of fracture repair without considering removal of wrong adhesion of hepatic veins and portal veins; 3) considering possible wrong adhesion for removing the hepatic vein and the portal vein, setting a stricter connection condition to obtain a broken blood vessel center line, and solving a broken blood vessel center line (2) in which the hepatic vein and the portal vein are obviously distinguished by taking classification as a purpose; and 4) combining the center lines (1) and (2), reconstructing missing blood vessel segments in the original blood vessel segmentation mask, and combining the reconstructed blood vessel segments into the original segmentation mask to obtain a final blood vessel mask and center line topological structure after fracture repair.

The present invention discloses a method and system for enhancing liver CT images. The method includes: collecting N liver CT images of a specific liver taken from different cross-sections in the plain scan phase and the enhanced phase respectively, where 50 ≤ N ≤ 200; respectively processing and enhancing m liver CT images in the plain scan phase and m liver CT images in the enhanced phase by the filtered back projection method to obtain A-class enhanced images in the plain scan phase and A-class enhanced images in the enhanced phase, where m < N; respectively processing and enhancing the remaining k liver CT images in the plain scan phase and k liver CT images in the enhanced phase by the adaptive statistical iterative reconstruction method to obtain B-class enhanced images in the plain scan phase and B-class enhanced images in the enhanced phase, where k = N - m; and respectively integrating the A-class enhanced images in the plain scan phase with the B-class enhanced images in the plain scan phase, and integrating the A-class enhanced images in the enhanced phase with the B-class enhanced images in the enhanced phase, and jointly constructing a three-dimensional liver model.

The invention provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention relates to the technical field of liver image processing, in particular to an early screening model training method for non-alcoholic fatty liver. According to the method, the fat infiltration region is effectively screened out through the gray value of the pixel region and the region shape feature, and the fat layer saliency is obtained. And processing in the gray level histogram, analyzing the probability of each peak as a fibrosis peak except the highest peak, and finally judging the fibrosis peak and obtaining the fibrosis significance. Through the two features, automatic marking of the liver CT image is realized. And the early screening model of the non-alcoholic fatty liver can be trained through the automatically marked liver CT image. According to the embodiment of the invention, the features in the liver CT image are identified, through automatic marking, training of a non-alcoholic fatty liver early screening model is facilitated, and the training efficiency and the model accuracy are ensured.

The invention discloses a neural network model for liver CT image segmentation and a liver cancer classification method based on the neural network model. The neural network model adopts a feature encoder for performing down-sampling feature extraction on the liver CT image; a UNet + + model of a multi-scale feature fusion module connected between the feature encoders and the feature decoders and used for aggregating feature information of different levels, wherein the feature decoders are used for performing up-sampling on the features extracted by the feature encoders to recover spatial dimensions; the feature encoder is integrated with a high-efficiency channel attention module used for enhancing attention to a liver focus key area and inhibiting irrelevant background tissue information and a residual module used for maintaining a network gradient flow; through aggregation and fusion of different levels of feature information, more detail features are reserved when the image space dimension is restored, so that the segmentation precision of the liver and the lesion boundary thereof is remarkably improved, and the segmentation result is more accurate and robust.

The application discloses a liver US-CT medical image conversion method based on a CycleGAN network. The method comprises the following steps: inputting a liver US image x as a training sample into a generator G; the generator G generates a liver pseudo-CT image y' according to the liver US image x; inputting the liver pseudo-CT image y' as a training sample into a generator F; the generator F generates a loopback liver US image x'' according to the liver pseudo-CT image y'; training the generator G; training a discriminator Dy; inputting a liver CT image y as a training sample into the generator F; the generator G generates a liver pseudo-US image x' according to the liver CT image y; inputting the generated liver pseudo-US image x' as a training sample into the generator G; the generator G generates a loopback liver CT image y'' according to the liver pseudo-US image x'; training the generator F; and the discriminator Dx.

The application provides an unsupervised liver tumor CT image segmentation method based on manual features, relates to the technical field of deep learning, and first collects normal liver CT images as a data set, and selects a tumor position in combination with clinical knowledge. A texture similar to real imaging is generated through three-dimensional simple noise binary mask, and histological features are used for morphological modeling. Then, the tumor texture is superimposed with the liver CT image at the selected position to synthesize a new liver tumor CT image. A segmentation model is trained by using the CT image with annotations, and the synthesized image is used for medical image segmentation. Finally, the segmentation results are evaluated by using the Dice coefficient, the Hausdorff distance, the standardized surface distance and the surface distance index. The application is helpful for artificially synthesizing tumor lesion images and generating voxel-level annotations, and provides a new perspective for solving the medical image annotation challenge and promoting liver tumor diagnosis and treatment.

The invention provides a domain conversion coding and decoding lesion segmentation network method and system for a liver CT image. The domain conversion coding and decoding lesion segmentation network method for the liver CT image comprises the following steps: constructing a generative adversarial network coding and decoding lesion segmentation network joint model; and performing focus segmentation on the HCC focus by using the joint model. According to the technical scheme, the problem that the deep learning model is unstable due to the image data diversity problem and the data size problem is solved, and the stability and efficiency of the focus segmentation model are improved.

This invention discloses a two-stage controllable liver CT image generation method combining GAN and Diffusion models. The first step involves segmenting a real CT image using a pre-trained nnU-Net to obtain liver and tumor masks. The second step involves training a VQ-VAE model to establish a mapping between pixel space and latent space; training a conditional LDM to perform incisive generation of liver regions: predicting noise under the guidance of multiple hot tags of tumor size, unmasked region features, and the mask. The third step involves rapidly generating a full-frame CT image with global anatomical consistency using GAN; refining liver regions lacking texture detail using the trained LDM; and replacing the liver regions in the full-frame CT image after VQ-VAE decoding. This method combines the global synthesis efficiency of GAN with the local fine-grained generation capability of the diffusion model, effectively improving the realism and diversity of the final synthesized image while significantly reducing computational resources.

The invention discloses a method and a system for planning twinborn before personalized hepatic resection. On one hand, the personalized preoperative twinborn planning method for the hepatic resection comprises the following steps: acquiring preoperative information of a target patient to construct a predetermined patient liver twinborn three-dimensional model; a liver CT image three-dimensional model is generated, and the volumes of the liver and the focus are obtained through calculation; classification and integration of the information are implemented so that clinicians can call the information conveniently; rapidly measuring to obtain the size of the liver lesion part of the patient; a liver resection treatment simulated twinborn model is constructed to display a predetermined anatomical structure of the liver of a patient to assist a clinician in preoperative planning of a personalized surgical plan. On the other hand, the invention provides a personalized twin planning system before the hepatic resection, wherein the personalized twin planning method before the hepatic resection is adopted by the personalized twin planning system before the hepatic resection. The method has the advantage that preoperative patient information can be integrated to construct the liver resection treatment simulation twinborn model so as to assist clinical doctors in preoperative planning of personalized surgical plans.

The present invention provides an intelligent detection method, an electronic device and a storage medium for liver echinococcosis CT images based on domain adaptation, comprising the following steps: collecting liver CT image data; performing image preprocessing based on a multi-scale structure-preserving enhancement model guided by a gradient flow field; performing target detection annotation; and training a model. Advantageous effects of the present invention: This method can effectively reduce the influence of different CT devices on the detection of liver echinococcosis by using domain adaptation technology, thereby improving the accuracy and consistency of AI-assisted doctor diagnosis. It can not only save medical costs, but also improve the medical experience and health management of patients in remote areas.

The invention relates to the technical field of artificial intelligence, in particular to a patient information analysis method based on multi-modal fusion, and the method comprises the steps: obtaining a target liver CT image of a patient, marking the target radiology characteristics of the target liver CT image and the target clinical examination information of the corresponding patient, coding the target liver CT image, and obtaining the image coding characteristics; the method comprises the following steps: marking and coding a target radiology feature to obtain a radiology coding feature, coding clinical examination information to obtain an examination information coding feature, fusing an image coding feature, the radiology coding feature and the examination information coding feature to obtain a cross-modal fusion feature, and using a classification head module in a trained deep learning model to carry out classification on the cross-modal fusion feature. And performing cirrhosis outcome classification on the cross-modal fusion features to obtain a cirrhosis outcome classification result. The fusion features of the liver information of the patient are subjected to outcome classification in an automatic fusion mode, so that the classification result of the liver cirrhosis outcome is predicted more accurately.

The present invention relates to the technical field of image enhancement, and particularly relates to a method and system for enhancing liver CT images. The invention obtains the initial window of each connected region; according to the density distribution of the pixel points in each initial window on the CT image, obtains the density change degree of each initial window; obtains the multi-scale images corresponding to the CT image, and according to the frequency domain distribution characteristics of the initial windows corresponding to the same position between different scale images, obtains the high-frequency information change degree of each initial window; further obtains the window correction coefficient of each initial window, corrects each initial window, and obtains the corrected window of each initial window; performs adaptive histogram equalization on different scale images according to the corrected window, and combines with the degree of chaos of the corresponding scale image for weighted fusion to obtain the enhanced CT image. The invention improves the effect of image enhancement by obtaining the accurate window size during image equalization processing.

The invention relates to a liver tumor segmentation method and device based on adaptive context awareness fusion, and the method comprises the steps: S1, obtaining and preprocessing liver CT data, and dividing the data into a training set, a verification set and a test set according to a proportion; s2, constructing an ACAF-Net (Adaptive Context-Aware Fusion Network), and constructing a double-enhanced dynamic convolution module in the transmission and fusion of the double paths; s3, fusing the double-enhanced dynamic convolution and the double attention weight, and constructing a double-dimensional mixed attention module; s4, fusing the sub-region segmentation error and the dynamic quantization difficulty, and constructing a double-error dynamic balance loss; s5, fusing the double-enhanced dynamic convolution module, the double-dimensional mixed attention module and the double-error dynamic trade-off loss to construct an ACAF-Net model; and S6, completing model training, verification optimization and performance evaluation by using the experimental data set. By using the method, the problems of fuzzy liver tumor boundary, large scale difference and unbalanced segmentation error are effectively solved, the segmentation precision and robustness are remarkably improved, and the method is suitable for automatic segmentation of the CT image liver tumor.

The application belongs to the technical field of CT image processing devices, and particularly relates to a lesion segmentation device based on liver CT images, a preprocessing module pre-processes liver CT images; a segmentation model establishing module establishes a CT image lesion area segmentation model, and then a training module is used to train the CT image lesion area segmentation model; finally, a segmentation module is used to segment the liver and the lesion area of the pre-processed liver CT image by using the trained CT image lesion area segmentation model. The liver CT image is pre-processed to adjust the CT image to a proper size and contrast, 3D convolution is used instead of 2D convolution in the training process of the CT image lesion area segmentation model to retain the connection between slices, [3*3] deep separable convolution is used to extract features, and [1*1] convolution is used to fuse features and adjust the number of channels, thereby effectively improving the accuracy of the segmentation of the lesion area in the CT image and improving the work efficiency of doctors.

The present invention discloses a method for intelligent segmentation and recognition of liver CT images, comprising the following steps: S1. Using a liver segmentation model to segment the liver parenchyma region of an abdominal CT image to obtain a liver mask; S2. Multiplying the liver mask with the original input abdominal CT image to obtain a liver localization image; S3. Inputting the liver localization image into the liver segmentation model, which then performs Couinaud segmentation on the liver region. The present invention also discloses an intelligent segmentation and recognition model for liver CT images. The present invention can achieve more accurate liver segmentation recognition results.

The present application relates to a liver CT image segmentation method based on global self-attention and multi-scale feature fusion, belonging to the technical field of medical image processing. The present application comprises the following steps: (1) obtaining an abdominal CT data set and performing pretreatment; (2) using a ResNeXt convolutional neural network to extract multi-scale features and introducing multi-scale spatial information; (3) using the multi-scale features through a global self-attention module to obtain global self-attention fusion features; (4) extracting features through an improved convolution module for the fusion features, and finally up-sampling to obtain a segmentation result. The method is verified based on the LiTS public data set, and the average Dice value of the overlapping area of the segmentation result and the true segmentation reaches 96.4%, which is 4.3% higher than that of the classic model UNet.

The invention provides a liver CT image reconstruction method under a low-dose scanning condition, and relates to the field of image processing, and the method specifically comprises the steps: collecting a standard-dose liver CT image, and generating a low-dose liver CT image; based on a low-dose liver CT image, calculating channel feature difference in a neighborhood range to construct local structure potential energy, generating a structure driving force response value based on potential energy difference, selecting a maximum response direction as a feature aggregation direction, and performing feature fusion in combination with a jump aggregation control factor to obtain a structure aggregation feature map; performing morphological projection density and slope mapping calculation on the structure aggregation feature map, and obtaining a morphological modulation feature map through feature modulation and residual fusion; on the basis of the morphological modulation feature map, combining neighborhood gray difference expansion and multichannel displacement feature analysis, generating a signal modulation factor and performing nonlinear feature regulation and control to obtain an information migration modulation map; and inputting the information migration modulation graph into a reconstruction module for image reconstruction.

The present invention relates to the technical field of liver image processing, and in particular to a method for training an early screening model for non-alcoholic fatty liver disease. The method effectively screens out fat infiltration areas and obtains the significance of the fat layer through the grayscale value of the pixel area and the regional shape characteristics. Processing is performed in the grayscale histogram, and the probability of each peak as a fibrosis peak is analyzed except for the highest peak. Finally, the fibrosis peak is judged and the fibrosis significance is obtained. Automatic labeling of liver CT images is achieved through these two features. The early screening model for non-alcoholic fatty liver disease can be trained with the automatically labeled liver CT images. The embodiment of the present invention identifies features in liver CT images, and facilitates the training of the early screening model for non-alcoholic fatty liver disease through automatic labeling, thereby ensuring the efficiency of training and the accuracy of the model.

The application relates to the technical field of liver segmentation, in particular to a liver image segmentation method based on multi-head attention feature fusion, which comprises the following steps: establishing a liver image segmentation model based on an Unet network structure, collecting liver CT images and corresponding labeled region images; performing edge detection operator processing on the liver CT images to obtain a liver region contour image; performing binaryzation processing on the liver CT images to obtain a liver region binaryzation image; obtaining a liver region rough segmentation image through a segmentation network; after adjusting the liver CT images, the labeled region images, the liver region contour image, the liver region binaryzation image and the liver region rough segmentation image to the same size, the images are combined to obtain a combined image; training the liver image segmentation model by using the combined image set, calculating a loss by using a loss function, optimizing model parameters according to the loss until convergence; inputting a to-be-processed liver CT image into the trained liver image segmentation model to obtain a liver segmentation image; and the application can provide a more accurate segmentation image.

The invention provides a liver echinococcosis CT image intelligent detection method based on domain self-adaption, electronic equipment and a storage medium. The method comprises the following steps: collecting liver CT image data; carrying out image preprocessing on a multi-scale structure preserving enhancement model based on gradient flow field guidance; carrying out target detection labeling; and model training. The method has the beneficial effects that the influence of different CT equipment on liver echinococcosis detection can be effectively reduced by utilizing a field adaptive technology, so that the accuracy and consistency of AI-assisted doctor diagnosis are improved, the medical cost can be saved, and the medical experience and health management of patients in remote areas can be improved.

The application discloses a liver CT image continuity discontinuous dynamic imaging information fusion method, which combines a machine learning algorithm, filters and feature segments images by extracting and identifying feature points, obtains a dynamic change curve graph and a standard floating line, constructs a maximum loss difference value and a minimum loss difference value of the dynamic change curve graph, establishes a loss difference value allowable range, obtains minimum values and maximum values in a dynamic change period, judges threshold difference based on the maximum values, realizes liver CT image fusion image probability analysis, and solves the problems that in the prior liver change diagnosis and treatment process, doctors have heavy workloads, evaluation efficiency is relatively low, and analyzed lesion information is not comprehensive enough, so that key feature points are easily ignored, and diagnosis and treatment difficulty is increased.

The application discloses a liver tumor image segmentation method based on a CNN-Transformer hybrid architecture, and comprises the following steps: pre-processing a liver CT image to reduce the influence of image acquisition differences on a model; a multi-stage hybrid encoder effectively combines a convolutional neural network and a Transformer network, extracts local details, takes into account the dependency relationship between elements, and obtains multi-scale features; a feature enhancement decoder performs up-sampling through a convolutional neural network, realizes multi-scale feature fusion through an improved skip connection, and finally outputs a segmentation probability graph with a size of 512*512*3; and a post-processing module converts the segmentation probability graph into a segmentation result that can be directly used for surgery planning. The application constructs a powerful segmentation model that can grasp local details and understand global context, improves calculation efficiency, and reduces the consumption of computing power resources.

The invention discloses a liver CT image segmentation method, system and device, a medium and a product, and relates to the field of image processing, and the method comprises the steps: inputting a liver CT image in a case into an encoder, extracting and gradually downsampling image features, and flattening the highest-layer semantic features of the image features into a sequence; based on a Transform module, generating a preliminary hidden feature; a decoder is utilized to decode an image with preliminary hidden features for the first time, meanwhile, a cross fusion module is combined to carry out cross fusion processing on coding features from different convolutional layers of an encoder, the coding features are fused with up-sampling features, fused features are determined, edge slice features are determined by utilizing a slice sensing adjustment module, and the edge slice features are extracted by utilizing an image extraction module. Determining non-edge slice features by using a reverse attention module; the edge slice features and the non-edge slice features are reversely fused, the fused feature image is decoded for the second time, the segmentation result of the liver CT image is generated, and the performance of continuous CT image segmentation is improved.

The application provides a kind of liver CT image blood vessel segmentation mask fracture repair and topological analysis method, specific steps include: 1) skeletonization is carried out to liver blood vessel segmentation mask, and the blood vessel skeleton, skeleton end point and blood vessel radius rough value at skeleton point are extracted;2) without considering the removal of hepatic vein and portal vein error adhesion, for the purpose of fracture repair, obtain connected blood vessel center line ①;3) considering the removal of hepatic vein and portal vein possible error adhesion, set more strict connection condition, obtain the broken blood vessel center line, then for the purpose of classification, obtain the blood vessel center line ② containing fracture and obvious distinction of hepatic vein and portal vein;4) merge the above center line ① and ②, reconstruct the missing blood vessel segment in the original blood vessel segmentation mask, merge into the original segmentation mask, obtain the final fracture repair blood vessel mask and center line topological structure.

A method based on an r-IBS-introduced neural network for automatically determining the liver's condition from three common diseases—hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and fatty liver (FAT)—using liver CT images includes the following steps: First, construct an r-IBS-based training classifier. Use the classifier to determine the disease type for each liver CT image. Acquire liver CT images and process them using a snake model. Input the images into the trained classification neural network model. The neural network outputs the classification results for the aforementioned diseases.

The application provides an image automatic segmentation method based on a multi-level multi-attention MLMA-UNet network, solves the problems of high calculation complexity and low segmentation performance in the prior art, and comprises the following steps: step one: acquiring a liver CT image dataset and pre-processing the CT image; step two: constructing a multi-level feature recalibration network segmentation model for the liver and tumors, training the model by using a training set, extracting global and local features by using multi-level extraction, and recalibrating the channel response of the aggregated multi-level features; step three: adjusting the parameters of the multi-level feature recalibration network segmentation model and training the model multiple times, obtaining a stably converged model when the loss function of the model stably converges, testing the trained model by using a test set, constructing a liver and tumor detection network, obtaining liver and tumor segmentation results, and evaluating the network performance by using statistical metrics.

An early liver cancer diagnosis model based on CapsNet involves image processing. Features are extracted from the input enhanced liver CT image and processed through a ResNet module using convolution, regularization, activation functions, and max pooling. The output features are captured in the PrimaryCaps layer to obtain primary image features, reducing dimensionality and refining the features. An additional PrimaryCaps layer is added between the PrimaryCaps and DigitCaps layers to identify and integrate local features. After classification by the DigitCaps layer, a digital capsule layer is included for dynamic routing, and a Squash operation is performed to obtain a feature map composed of 8-dimensional vectors. E-CapsNet is designed by fusing the deep residual learning framework of ResNet and the high-level feature representation capabilities of CapsNet to improve the accuracy and efficiency of early liver cancer diagnosis.