60 results about "Abdominal ct" patented technology

The invention relates to the technical field of medical image processing, in particular to a colorectal cancer auxiliary staging method and system based on a CT image, and the method comprises the following steps: obtaining an abdominal CT image of a patient with colorectal cancer, and obtaining CT image data containing focus labeling information; preprocessing the CT image data containing the focus labeling information, and constructing a CT image data set; constructing an improved UNet network model based on the UNet network architecture, and performing training optimization on the improved UNet network model by adopting the CT image data set to obtain a trained improved UNet network model; and inputting a to-be-staging CT image into the trained improved UNet network model, and outputting a colorectal cancer T staging result corresponding to the to-be-staging CT image. According to the technical scheme of the invention, precise focus segmentation and automatic classification of T stages of the colorectal cancer CT image are realized.

The invention belongs to the technical field of image segmentation, and relates to a semi-supervised pancreatic image segmentation method based on prototype estimation and prototype consistency. According to the method, based on a semi-supervised segmentation framework of an average teacher, V-Net is adopted as a deep learning segmentation model, and a projection head is inserted in the last but one stage of a decoder as a prototype branch; prototype learning design prototype estimation is introduced, unmarked data prediction is divided into determined areas of fuzzy areas, and different losses are designed for areas with different reliability of the unmarked data. The problems that in an existing semi-supervised learning segmentation method, unmarked data cannot be fully utilized, potential information of marked data is insufficient in utilization and the like are effectively solved, and a higher-performance solution is provided for an abdominal CT image pancreatic organ segmentation task.

The invention discloses an abdominal CT image multi-view fusion method based on HU physical characteristic guidance, and relates to the technical field of medical image processing. The method comprises the following steps: firstly, constructing a three-dimensional HU volume, generating a semantic mask covering the whole HU range as physical prior, and obtaining two-dimensional slice sequences in the axial direction, the coronal direction and the sagittal direction through three-view projection; on the basis, multi-scale deformable alignment, HU-perceived deformation field fine correction, cross-view attention fusion and regional differentiation decision fusion are sequentially carried out, HU-perceived high-frequency residual enhancement and local contrast self-adaptive processing are carried out on two-dimensional slices, and finally a denser CT slice sequence is generated. According to the method, HU physical partition constraint is introduced in the multi-view alignment and fusion process, abnormal deformation and high-density structure distortion of a gas region are effectively inhibited, and geometric consistency and visual stability of an abdominal CT image in the interpolation reconstruction process are improved.

The invention discloses a CT-based osteoporosis intelligent diagnosis method and system, and relates to the technical field of medical image diagnosis, and the method comprises the steps: carrying out the positioning of a lumbar region in an abdomen or thoracic and abdominal CT image through a deep learning model; digitally modeling the shape and the internal structure of the vertebral body; and automatic diagnosis of osteoporosis is completed. According to the method, full-automatic positioning, accurate segmentation and intelligent diagnosis of osteoporosis of the lumbar region in a conventional abdomen or thoracic and abdominal CT image are realized, the accuracy and efficiency of diagnosis are remarkably improved, and the problems of strong subjectivity and poor repeatability caused by dependence on manual sketching or simple threshold segmentation in a traditional method are solved; the method can be directly operated based on clinical conventional CT data, is beneficial to large-scale osteoporosis screening and early intervention, and has remarkable clinical popularization significance and technical progress value.

The invention provides a postoperative cognitive impairment prediction system and method for gastrointestinal tumor surgery patients, and belongs to the technical field of postoperative cognitive impairment prediction of patients based on machine learning. The system comprises a data receiving and obtaining module, an abdomen CT feature extraction module, a patient clinical and information data coding module and a gastrointestinal tumor patient postoperative cognitive impairment prediction module. The data receiving and obtaining module extracts three-dimensional features of the abdomen image data to obtain an abdomen CT image fusion feature map; the patient clinical and information data coding module performs feature coding on the patient clinical data and the information data to obtain clinical feature vectors; and the gastrointestinal tumor patient postoperative cognitive impairment prediction module takes the abdominal CT image fusion feature map and the clinical feature vector as input and outputs a quantitative evaluation result of the postoperative cognitive impairment of the patient. According to the method, effective classification of cognitive impairment is realized, and powerful auxiliary support is provided for clinical intervention and management of postoperative patients.

The invention belongs to the field of medical image processing and artificial intelligence, and provides a colon cancer CT image segmentation method and system fusing individualized features, and the method comprises the steps: obtaining the abdomen CT images of a historical patient and a target patient; obtaining structured individual feature information related to the patient, wherein the individual feature information comprises age, gender, colon cancer family history and intestinal medical history information; preprocessing the abdomen CT image; and training an image segmentation model by using the preprocessed abdominal CT image of the historical patient, segmenting the preprocessed abdominal CT image of the target patient by using the trained image segmentation model meeting the segmentation precision requirement, and generating a colon cancer focus segmentation image conforming to the individual feature difference of the target patient. According to the method, the structured patient information is utilized to guide the model network to adjust the feature response, so that the segmentation precision and individual adaptability of the model are improved, and the segmentation accuracy is further improved.

The invention relates to the technical field of medical image segmentation, and discloses an abdominal medical image multi-organ segmentation method based on text guidance and MFF. The method comprises the following steps: firstly, constructing a lightweight image encoder, and extracting multi-scale image features of an abdominal CT image; secondly, a text encoder based on Transform is adopted to extract high-dimensional semantic features of the input text, and precise modeling of medical text semantics is achieved; then designing an image-text feature fusion module, and realizing deep interaction and semantic alignment of image and text features through a self-attention and cross-modal attention mechanism; and finally, providing a multi-scale feature decoder combined with a jump connection mechanism, fusing the multi-layer features and the multi-modal fusion features of the encoder, and outputting an abdomen multi-organ segmentation result and a target score of each organ. According to the method, the accuracy and robustness of complex abdominal organ segmentation can be effectively improved, and the multi-modal information fusion capability of the model is remarkably optimized while light weight is ensured.

The invention relates to the technical field of artificial intelligence, and provides an artificial intelligence assisted gallbladder image automatic detection method and system and electronic equipment, and the method comprises the steps: generating a corresponding generative image for an original abdomen CT image, and reconstructing the CT image of a gallbladder in a contraction state into a gallbladder-filled CT image; performing part identification on the generative image to determine whether a gallbladder part exists in the generative image; if the gall bladder part exists in the generative image, carrying out part identification on the original abdomen CT image to obtain a contour area of the gall bladder part; calculating the gallbladder volume according to the contour area of the gallbladder part, and detecting whether the gallbladder size is abnormal or not according to the gallbladder volume to obtain a gallbladder detection result. According to the invention, the automatic detection of the gall bladder image is realized, the rapid analysis of the CT image can be assisted, the influence of the current state of the gall bladder on the recognition accuracy of the gall bladder part is effectively avoided, and a more accurate gall bladder image detection result is rapidly obtained.

The application provides a liver fibrosis intelligent evaluation method and system based on an abdominal CT image, and the method comprises the following steps: extracting a liver region image from the abdominal CT image to construct 3D body data of the liver region; performing region segmentation on the body data to form a whole liver mask, a target region mask, and calculating the proportion of the volume of the target region to the volume of the liver, the target region being a left outer lobe upper segment and a left outer lobe lower segment region; when the proportion of the volume of the target region to the volume of the liver meets a preset threshold interval, extracting effective surface vertices of the target region and constructing a surface mesh; marking the nodule vertices in the surface mesh and weighting the heights of the nodule vertices, performing quadratic polynomial surface fitting on the weighted surface mesh to obtain a smooth reference surface; performing multi-dimensional surface feature extraction on the real surface mesh based on the smooth reference surface; and performing liver surface nodule scoring and liver fibrosis staging evaluation according to the multi-dimensional surface features.

The invention provides an intelligent evaluation method and system for hepatic fibrosis based on an abdominal CT image, and the method comprises the steps: extracting a liver region image from the abdominal CT image, and constructing the 3D volume data of a liver region; performing region segmentation on the volume data to form a whole liver mask and a target region mask, and calculating the proportion of the volume of a target region to the volume of the liver, the target region being a left outer lobe upper section region and a left outer lobe lower section region; when the proportion of the volume of the target area to the volume of the liver meets a preset threshold interval, extracting effective surface vertexes of the target area and constructing a surface grid; nodule vertexes in the surface grid are marked, the height of each nodule vertex is weighted, and quadratic polynomial surface fitting is carried out on the weighted surface grid to obtain a smooth reference surface; performing multi-dimensional surface feature extraction on the real surface grid based on the smooth reference surface; and performing liver surface nodule scoring and liver fibrosis staging evaluation according to the multi-dimensional surface features.

Partial supervised multi-organ automatic segmentation method and device for abdominal CT sequence images. The method includes: (1) obtaining a dataset of abdominal multi-organ CT sequence images to be segmented; (2) preprocessing the obtained dataset of abdominal multi-organ CT sequence images; (3) dividing the preprocessed dataset of abdominal multi-organ CT sequence images, which includes a training set, a validation set, and a test set; (4) constructing a deep learning model for abdominal multi-organ CT sequence image segmentation, and the deep learning model is a U-shaped network based on Swin Transformer technology; (5) training the deep learning model using the above training set and partial supervised loss, and saving the best model according to the above validation set, where the partial supervised loss is a linear combination of margin loss and exclusive loss; (6) using the above saved best model to predict the above test set to obtain segmentation results of multiple organs in the abdominal CT sequence images.

The invention discloses a Beijing duck liver volume calculation method based on a CT image, and belongs to the technical field of CT image processing. Comprising the following steps: acquiring a CT scanning sequence of a living Beijing duck, and constructing a Beijing duck abdomen CT image in a PNG format; marking whether the CT image of the abdomen of the Beijing duck contains the liver or not, and performing classification based on a high-performance classification model ShuffleNetV2 to obtain a CT image only containing the liver; performing liver region labeling on the CT image only containing the liver, and performing segmentation processing by using the improved lightweight neural network model MSDAUNet + + to obtain a liver region segmentation result of the CT image; and calculating the predicted volume of the liver and a correlation index between the predicted volume of the liver and the real volume. According to the method, more low-level detail information can be obtained, target area features with variable sizes in the image can be effectively captured, the segmentation precision of the model is improved, and the speed and performance of liver volume calculation are further improved.

The invention belongs to the technical field of pressurizing belts, and discloses a pressurizing belt for thoracico-abdominal CT examination, which comprises a chest belt, an adhesive belt, an adhesive surface, a magic tape, an abdominal belt and a dehumidifying and degerming paste, sticking belts are connected to left and right sides of the chest belt; the right side of the adhesive tape is glued with the adhesive surface. The bottom of the chest belt is connected with the belly belt through magic tapes. The center of the chest belt and the belly belt are glued with dehumidifying and sterilizing pastes. The pressurizing belt for thoracico-abdominal CT examination is used for thoracico-abdominal CT examination of a patient with insufficient breath, artifacts caused by respiratory movement are effectively reduced, and therefore the definition and accuracy of abdominal visceral organ development are improved. The belly belt and the chest belt are integrated, the front is divided into the upper part and the lower part, and the belly belt and the chest belt can be used separately through the hook-and-loop fasteners.

This invention discloses a pancreas segmentation method based on the WEUnet network, belonging to the field of pancreas segmentation technology. It solves the problem of unclear edges in pancreas segmentation. The method includes the following steps: S1, acquiring an abdominal CT image and inputting it into the WEUnet network; S2, performing five consecutive convolutional encoding processes on the abdominal CT image through the encoding module to obtain the encoded visual feature maps output by each layer of the encoding module; S3, inputting the bottom-level encoded visual feature map into the decoding module to obtain the bottom-level decoded prediction feature map; S4, performing four consecutive convolutional decoding processes on the bottom-level decoded prediction feature map through the decoding module to obtain the top-level decoded prediction feature map; S5, obtaining the pancreas image based on the top-level decoded prediction feature map. This network effectively solves the problems of pancreas deformation, unclear edges, and class imbalance, and further improves the segmentation index.

The present invention discloses a pancreatic ductal carcinoma prognosis survival analysis method based on a dual-branch adaptive network, comprising: first, using multi-center clinical abdominal CT images as a dataset, preprocessing the abdominal CT and performing coarse segmentation to achieve the localization of the pancreas and pancreatic tumors; second, using the segmentation network to construct a pancreas-pancreatic tumor parallel branch to obtain the anatomical characteristics of the pancreas and pancreatic tumors; then, using encoders to extract features from the dual branches respectively, and using a dual-stage pre-training-fine-tuning adaptive training method in the feature extraction stage to deeply extract the anatomical features of the pancreas and pancreatic tumors; finally, the spatial position relationship of the pancreas and pancreatic tumors plays a crucial role in prognostic analysis. A branch-guided fusion module is used to comprehensively capture the complex and diverse spatial position relative relationships of the pancreas and pancreatic tumors, and the output is input into the prognostic network to obtain the final pancreatic ductal carcinoma prognosis survival analysis results.

The application discloses a kind of multi-modal gastric cancer risk prediction method and system based on gate attention mechanism, it is related to gastric cancer risk prediction technical field, including, to abdominal CT image, gastric organ recognition result and health text information preprocessing, fusion standardization CT image and gastric organ recognition result, construct organ priori enhancement image.To organ priori enhancement image and health text information extract image feature and text feature, respectively calculate self-attention and cross-attention, according to self-attention entropy and cross-attention peak value generation image and the gate fusion coefficient of text, the weighted fusion of the output of two kinds of attention obtains gate fusion feature, and then calculates self-attention and cross-attention and is added in equal proportion to obtain fusion feature, and the risk of target sample suffering from gastric cancer is classified and output.The application makes image feature and text feature complement each other, improves the accuracy and stability of gastric cancer risk prediction, is suitable for health management auxiliary, and is not used as clinical diagnosis basis.

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 application discloses a method for abdominal CT image segmentation based on branch growth neural network architecture search, comprising the following steps: constructing a search space network based on local width branch and global depth branch; alternately connecting the local width branch and the global depth branch to the growable down-sampling layer of an encoder based on the search space network to construct a grown candidate network; training and testing the grown candidate network to obtain an image segmentation model; inputting the abdominal CT image data to be tested into the image segmentation model to complete abdominal CT image segmentation. The application improves the accuracy of abdominal CT image segmentation.

The application discloses a pancreas image segmentation method and system based on deep learning. The method comprises the following steps: acquiring an abdominal CT image sequence and performing window width and window level adjustment, resampling and region of interest cropping; loading pre-trained and fixed weight encoder weight and joint segmentation network model parameter, performing single forward propagation inference on the preprocessed image to obtain a segmentation probability map and a predicted shape vector; post-processing the segmentation probability map to output a three-dimensional pancreas segmentation mask; loading shape prior knowledge base, and combining the predicted shape vector to perform offline confidence evaluation on the segmentation result. The application effectively solves the segmentation fracture problem caused by the fuzzy pancreas boundary by fusing global shape prior constraints, and quantifies the reliability of the result through the confidence evaluation mechanism, thereby improving the precision and clinical applicability of pancreas segmentation.

The present invention provides a method, system, medium, and electronic device for automatically segmenting blood vessels around organs. The method comprises the following steps: obtaining a user's enhanced abdominal CT image and extracting an image around a target organ; constructing multiple angiography-driven perturbation images based on the image around the target organ; constructing perturbation features that simulate false correlations based on the image around the target organ; and training an automatic blood vessel segmentation model based on the image around the target organ, the perturbation images, and the perturbation features, thereby obtaining a blood vessel segmentation result around the target organ based on the trained automatic blood vessel segmentation model. The method, system, medium, and electronic device for automatically segmenting blood vessels around organs constructed by the present invention construct an automatic blood vessel segmentation model based on image-level and feature-level causal intervention schemes, effectively enhancing the generalization performance of the automatic blood vessel segmentation model and improving the reliability of the blood vessel segmentation results.

This invention discloses a method and system for assessing postoperative abdominal organ ischemia risk based on image analysis, belonging to the field of image analysis technology. The method includes acquiring enhanced abdominal CT images of the target patient and outputting them after standardization processing; employing an improved 3D U-Net++ architecture with multi-task collaborative learning to jointly segment the target patient's organs and target regions, integrating geometric priors and topological inference mechanisms; its key technical points are: using multi-task segmentation to focus RPPR calculation on the real ischemic area, avoiding average dilution of the signal across all organs, and topological correction to ensure that vascular features reflect the real anatomy; furthermore, through the joint analysis of low-perfusion area prediction masks and vascular VTIF, it reveals two ischemic subtypes: structural occlusion and functional hypoperfusion, promoting the individualization of clinical intervention strategies and defining the necessary vascular intervention or conservative treatment, making the overall solution both innovative and clinically applicable.

The present invention discloses a method for multi-organ segmentation of abdominal CT images based on deep learning. The method is specifically implemented as follows: (1) constructing a training dataset containing abdominal CT images and their corresponding multi-organ segmentation results; (2) designing a segmentation network based on a dual self-attention mechanism and multi-scale feature fusion; (3) constructing a network loss function by combining Dice loss and Focal loss; (4) training the network using the training dataset; and (5) using the trained network to segment the various organ regions in the abdominal CT images. By adopting the dual attention and multi-scale feature fusion mechanisms, the present invention can establish long-range dependencies in a more targeted manner while introducing a very small number of parameters and floating-point calculations, thereby solving the problem of low segmentation accuracy of irregularly shaped long organs.

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 an arm fixing and thoracic and abdominal cavity external expansion device suitable for thoracic and abdominal CT scanning, which is based on an examination bed for thoracic and abdominal CT scanning and comprises a base platform, an upper limb supporting component, an upper limb fixing component, a headrest and a scapula wedge pad, the base platform is horizontally arranged on the examination bed, an upper limb fixing part, a headrest and a scapula wedge pad are arranged on the base platform, and the upper limb fixing part is located at the front end of the base platform and comprises a fixing wrist strap; the headrest and the upper limb fixing part are correspondingly arranged, the upper limb supporting part comprises a left supporting arm, a right supporting arm and an elastic band, and the left supporting arm and the right supporting arm are arranged on the two sides of the base platform in a mirror symmetry mode respectively. When the arm fixing and thoracic and abdominal cavity external expansion device suitable for thoracic and abdominal CT scanning is used, upper limb uplifting, stable fixing, thorax abduction and thyroid protection can be achieved. According to the invention, the problem of artifacts caused by unstable body position of a traditional device can be solved.

The invention relates to the technical field of image data processing, in particular to a CT image feature extraction method for gastrointestinal stromal tumor risk stratification, which comprises the following steps of: firstly, acquiring an abdominal CT plane scanning period image and a venous period image of a gastrointestinal stromal tumor patient, and performing coarse segmentation by utilizing gray scale difference of the two periods to obtain a tumor suspected area; a suspected content area is identified by analyzing gray change characteristics of the edge of the area, whether the suspected content area is a real gastrointestinal tract content (such as a contrast agent or bubbles) is further verified, the confirmed content is removed, a pure gastrointestinal stromal tumor area is finally obtained, and radiomics characteristics for risk stratification are extracted from the pure gastrointestinal stromal tumor area. According to the scheme, interference of gastrointestinal tract contents on tumor boundary judgment and internal texture analysis is effectively eliminated, so that the segmentation result is more accurate, high-quality data is provided for image omics feature extraction, the accuracy and reliability of risk layering are enhanced, and preoperative noninvasive accurate evaluation is realized.

The invention discloses a colorectal cancer early diagnosis system and method based on a CT image, and the system obtains a CT image of the abdomen of a patient through an image obtaining module, carries out the segmentation of a colorectal part through an image segmentation module, and generates an image mask. The periintestinal fat mask is extracted through the periintestinal fat extraction module, and the dissimilatory degree and the confusion degree of the periintestinal fat mask are calculated. The system constructs a comprehensive diagnosis index in combination with the BMI index of the patient, and evaluates the colorectal cancer risk through a threshold determination module. The method is implemented based on the system. According to the invention, early-stage, non-invasive and high-sensitivity colorectal cancer diagnosis is realized, and the kit has important clinical application value.

The present invention discloses a method for automatic and accurate segmentation of the liver region in abdominal CT sequence images, which mainly includes: (1) for the CT sequence to be detected, first reconstructing two-dimensional slices from three viewing directions: sagittal, coronal, and transverse; (2) using a U-shaped 2D convolutional network based on dilated spatial pyramid convolution to segment the two-dimensional slices in different viewing directions; (3) using a lightweight 3D convolutional network to fuse the segmentation results from different viewing directions to obtain the probability that each voxel in the CT sequence belongs to the target; (5) constructing a fully connected conditional random field energy function based on the obtained probability, and obtaining an accurate liver segmentation result by minimizing the energy function. The present invention extracts the three-dimensional features of the CT sequence by fusing information from different viewing directions, and obtains a three-dimensional liver segmentation result with high accuracy and strong robustness by introducing a fully connected conditional random field.

The application discloses a CT image enhancement processing method for tumor boundary recognition, and relates to the technical field of medical image processing. The method comprises the following steps: S1, performing multi-scale decomposition on an original abdominal CT image, and obtaining a preprocessed coarse scale component and a preprocessed fine scale component through a multi-scale feature decoupling network; S2, fusing the preprocessed coarse scale component and the preprocessed fine scale component, obtaining a corresponding CT enhanced image, and obtaining an initial boundary contour of the CT enhanced image; and S3, according to the initial boundary contour, obtaining an uncertainty score corresponding to each sampling point, determining a corresponding low-confidence boundary segment, and performing decomposition and enhancement processing on the low-confidence boundary segment to obtain a final tumor boundary contour. The application can specifically correct unreliable parts in the boundary contour, significantly improves the clinical reliability and robustness of the final tumor boundary contour, and avoids overall boundary errors caused by local blurring or noise.

The present invention discloses a liver segmentation method based on a multi-scale semantic feature network, comprising the following steps: selecting an existing abdominal CT public dataset image and preprocessing the images in the dataset; building a network model based on the multi-scale semantic feature network structure; sending the preprocessed images in batches to the built multi-scale semantic feature network model to train the model; preprocessing the abdominal CT images to be segmented; inputting the preprocessed abdominal CT images to be segmented into the trained multi-scale semantic feature network model, which outputs a segmented image of the liver region. The present invention employs PLSANet to extract multi-scale semantic features, thereby resolving the problem of the large scale of the network model and improving the operational efficiency of the model; the present invention integrates multi-scale semantic feature information, which is beneficial for reducing information loss generated during the convolution calculation process, making the network more sensitive to small target areas, and improving the segmentation accuracy of the liver region.

The application discloses a kind of medical image segmentation methods and systems for abdominal CT image, it is related to medical image processing technical field, including: obtaining three-dimensional CT body data, is divided into training dataset and inference dataset;Training data set is input into segmentation model and is trained, and the segmentation model after training is obtained;Inference data set is input into the segmentation model after training, executes segmentation step and obtains class probability graph, class label graph is obtained by processing class probability graph, finally obtains three-dimensional segmentation body;Three-dimensional segmentation body is post-processed using connected domain post-processing rule, and finally multi-class segmentation result is obtained.The application adopts the above-mentioned medical image segmentation method and system for abdominal CT image, through the joint design of training stage, inference stage and post-processing stage, improve the segmentation precision and output stability of complex boundary region, small organ region and target organ overall structure.