91 results about "Abdominal ct" patented technology

The invention relates to an abdominal CT multi-organ segmentation method based on a LoRA vertical domain MedSAM large model. The method comprises the following steps: step 1, preprocessing an abdominal CT image supervised learning data set; step 2, constructing a feature selection network; step 3, constructing a LoRA layer; step 4, performing interlayer context reasoning by using a mixed prompt strategy to realize abdomen CT automatic segmentation; the method has the advantages that through multi-feature information fusion, dynamic context prompting and self-adaptive LoRA fine tuning, the precision and robustness of the MedSAM large model in the abdomen multi-organ CT segmentation task are improved, multi-feature information can comprehensively represent organ features, context information can be effectively utilized through dynamic context prompting, and the accuracy and robustness of the multi-organ CT segmentation task are improved. Limited training data can be effectively utilized by self-adaptive LoRA fine tuning; according to an image encoder based on a MedSAM large model, a learnable LoRA layer is added to each Transform layer so as to achieve efficient parameter training, and for a mask decoder of the MedSAM large model, the LoRA layers are also added to a self-attention layer and a cross-attention layer at the same time.

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 pancreatic wrapped necrosis endoscope debridement precise navigation method and system, and relates to the technical field of endoscope navigation. Comprising the following steps: acquiring a plurality of historical abdominal CT images before endoscopic drainage debridement, and identifying contour areas of a pancreatic head, a pancreatic neck, a gland, a pancreatic tail, a duodenum, a gastric antrum and a gastric body in the abdominal CT images; an endoscope image and an ultrasonic image in the endoscope drainage debridement process are obtained, a gastroscope part is recognized, and a pancreas area is recognized; constructing a feature vector, inputting the feature vector into a pre-trained CNN network model, and obtaining an abdominal CT image with the highest matching degree with a pancreas region recognition result in the current ultrasonic image from a plurality of historical abdominal CT images; and carrying out abdominal CT multi-plane reconstruction and display, so as to realize positioning and navigation of pancreatic wrapped necrosis endoscope debridement. According to the invention, through feature fusion processing of the endoscopic image, the ultrasonic image and the CT image, accuracy and safety required by an operation are ensured.

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 provides a colorectal cancer peritoneal metastasis prediction model combining a convolutional neural network (CNN) and a Transformer model, and aims to improve the accuracy and efficiency of early detection and diagnosis of colorectal cancer. By collecting abdominal CT image data of a colorectal cancer patient before an operation and processing the data, a deep learning network based on CNN and Swin-Transformer is constructed, and the model can automatically extract and learn important features in the image. And after pre-training the model, further training the model so as to accurately predict the peritoneal metastasis risk of the patient. According to the model, higher prediction accuracy, automatic feature extraction and a more efficient diagnosis process are realized, and application in the fields of colorectal cancer risk prediction and prevention and control is facilitated.

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 discloses an abdomen CT image segmentation method and system based on deep learning, a terminal and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-segmented abdomen multi-organ CT image; preprocessing the abdomen multi-organ CT image to obtain a preprocessed image; inputting the preprocessed image into a trained cascade convolution decoder segmentation network model for organ segmentation to obtain an abdominal organ CT image segmentation label image; and outputting the segmentation label image of the abdominal organ CT image. According to the method, the feature extraction capability of the segmentation model is enhanced, the segmentation precision of the abdominal CT image is improved, a doctor can be assisted to better observe the abdominal CT image, the observation experience requirement on the doctor is reduced, and the diagnosis efficiency and accuracy are improved.

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.

The invention relates to the field of medical image processing, and discloses a liver hardness degree automatic evaluation method and system based on an abdominal CT image, and the method comprises the steps: carrying out the preprocessing of a CT image, and segmenting the preprocessed CT image into a plurality of patches; constructing a neural network model, extracting a training data set, training the network model, inputting the segmented CT image, and outputting a reasoning result of each patch; according to the reasoning result of each patch, combining the reasoning results into the reasoning result of the liver volume in the whole image; based on the liver volume reasoning result, obtaining the edge of the liver in the CT image; performing LSN measurement according to the edge of the liver; and evaluating the liver hardness degree according to the LSN measurement result. According to the method, the liver and the reasonable liver edge are automatically recognized based on AI, the whole process is automatically processed by the system, and the requirements for professional physicians, training before software is used and cumbersome operation processes are omitted.

The present invention discloses a liver segmentation method for abdominal CT images, including obtaining original abdominal CT image data and liver segmentation data; constructing a model training set and a model test set; constructing an original liver segmentation model; constructing a training loss function; using the model training set and the model test set, training and testing the original liver segmentation model based on the training loss function and obtaining a liver segmentation model; using the liver segmentation model to segment the liver of a target abdominal CT image, and performing morphological optimization to obtain a final liver segmentation result of the target abdominal CT image. The present invention also discloses a CT imaging method including the above-mentioned liver segmentation method for abdominal CT images. The present invention can improve the continuity between slices of CT sequence liver segmentation results, realize accurate liver segmentation in end-to-end abdominal CT images, improve boundary accuracy, and has strong robustness, good segmentation effect and good reliability.

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 present invention discloses an automatic segmentation method, device and medium for abdominal three-dimensional CT images, including obtaining an abdominal CT image data set for preprocessing, using an improved U-Net network model for coarse segmentation, using an SOA-Net network model for fine segmentation of four regions divided according to the approximate position information of each abdominal organ, and replacing the segmented image into an abdominal 3D scan image by elements to obtain an abdominal segmented image. The abdominal three-dimensional CT image is coarsely segmented by an improved U-Net network model, and then finely segmented by an SOA-Net network model that uses the least parameters and floating-point operations to obtain better segmentation accuracy, to obtain an accurate abdominal organ image, which meets the requirements for accurate and efficient segmentation of abdominal CT images in clinical practice, and solves the problem that the existing abdominal CT image segmentation method cannot accurately and efficiently segment abdominal CT images in clinical practice.

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 present invention relates to the field of image processing, and in particular to a method, system, storage medium and electronic device for optimizing the recognition of bone images. A method for optimizing the recognition of bone images comprises: stacking an abdominal CT image containing a detection frame of a target bone region to obtain a first image; inputting the abdominal CT image into a segmentation model to obtain a rib segmentation result; performing non-intersection filtering based on the first image and the rib segmentation result to obtain a filtering result, and using the filtering result as the recognition result of the target bone region. The present invention can optimize the existing imaging method to make it more intuitive, and can also accurately locate the bone region, thereby increasing the user experience and making it more convenient to know the part of the body where the bone region is located.

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 invention discloses a liver tumor segmentation method based on multidirectional spatial features. The method aims at achieving accurate liver tumor segmentation. The method comprises the following steps: S1, preprocessing abdominal CT data to obtain NPY matrix data, and dividing the data into a training set, a verification set and a test set; s2, extracting initial features of NPY matrix data; s3, extracting multidirectional spatial features from the initial features; s4, constructing an adaptive gating attention module AGAM to perform feature extraction on the multidirectional spatial features to obtain local features; s5, constructing a six-way Mamba module HoM to perform feature extraction on the multidirectional spatial features to obtain global features; s6, fusing and enhancing the local features and the global features to obtain enhanced features; s7, constructing a channel analytic entropy module (CPEM) to screen the enhanced features to obtain final features; and S8, splicing the multi-scale final features to obtain a segmentation map of the liver tumor. The method can effectively improve the liver tumor segmentation precision, and can be used for liver tumor segmentation in a CT image.

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.