150 results about "Multi organ" patented technology

The invention provides an abdomen multi-organ image segmentation method fusing multi-scale features, and the method comprises the steps: S1, constructing a space detail fusion module extraction network, which comprises a space attention module, a channel attention module, an up-sampling operation, a down-sampling operation and a feature smoothing operation; s2, designing an encoder, and connecting the encoder with the space detail fusion module; s3, transmitting a plurality of features with different resolutions obtained by the encoder to a decoder through jump connection for information recovery; and S4, arranging the abdominal multi-organ medical image data set to perform model training, and performing multi-organ segmentation prediction on the abdominal multi-organ medical image. According to the method, local and global information is fully utilized, multi-scale feature information is combined, the image feature extraction and expression capability of the model is improved, and the segmentation precision of multiple organs of the abdomen is improved.

The invention discloses a multi-scale feature fusion triple branch network method for multi-organ segmentation, and the method comprises the following steps: extracting local features through a CNN, and extracting global features through Transform; the features extracted by the CNN and the Transfomer are processed by using space attention and channel attention respectively, the processed features are fused, and output features are obtained through a feature processing module; the method comprises the following steps: setting a mask mechanism Smart Mask, improving a fixed mask mode in a Swin Transform, and capturing a long-distance dependency relationship; according to the method, global features in a medical image are learned through parallel Vision Mama, features obtained after CNN and Transform fusion are guided, and a segmented image is obtained. The CNN, Transform and Mamba encoders are combined, so that efficient medical image segmentation is realized. And the introduced parallel Vision Mama layer increases a small number of parameters and enhances the global context capture capability at the same time, so that the performance and efficiency of the model are remarkably improved.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention relates to the technical field of artificial intelligence, and discloses a method and a system for dynamically predicting the risk of harm of feed mycotoxin to livestock and poultry. The method comprises the following steps: constructing a metabolic digital twin model based on a multi-organ coupling dynamics mechanism, connecting a gastrointestinal tract, a liver and a kidney through blood flow parameters to form a closed loop topology network, establishing a toxin migration rate equation, and verifying bioavailability; collecting feed toxin concentration and livestock and poultry physiological data in real time; after distributing the initial toxin load, iteratively generating target organ dynamic concentration distribution by adopting a Runge-Kutta method; an organ exposure index is calculated based on an organ specific hazard threshold, key organs are positioned, and low / high risk levels and toxin accumulation paths are output. The method solves the problems that a traditional static model is low in efficiency and poor in organ difference adaptability when being used for dynamically predicting the risk of harm to livestock and poultry caused by feed mycotoxin.

The invention discloses a multi-organ medical image segmentation method based on a multi-feature fusion Swindow-Unet architecture, and belongs to the field of medical image processing. The core of the method is that CT and MRI images are input into a pre-trained CMFSA-UNet model for segmentation, and the model comprises an encoder, an MAFR module, an MFDF module, a decoder and a jump connection layer. CNN-Swin Transform double branches are adopted by the encoder, local details and long-range semantics are extracted, and Attention Gate reinforcement is carried out; the MAFR module widens a receptive field through double branches, combines an attention mechanism with residual connection, reduces the calculated amount and gives consideration to local and global features; and the MFDF module fuses multi-scale dense connection and frequency domain processing, so that feature loss is reduced. The decoder extracts features through Swin Transform Block, resolutions are recovered through 4 times of up-sampling, and the segmentation precision is optimized in combination with depth supervision and a mixed loss function. According to the method, local and long-range feature modeling is efficiently cooperated, precision and efficiency are balanced, segmentation global consistency, boundary accuracy and training stability are improved, the method is suitable for multi-modal multi-organ segmentation, and reliable support is provided for clinical diagnosis and the like.

A method (100) for recommending a patient treatment comprising: (i) receiving (120) information about the patient, wherein the information comprises a plurality of patient outcome prediction features; (ii) extracting (130) the plurality of patient outcome prediction features from the received information; (iii) analyzing (140), using a trained time-varying treatment effect model, the plurality of patient outcome prediction features to predict a plurality of different outcomes for the patient, wherein each of the plurality of different outcomes is associated with a patient treatment leading to said respective outcome; (iv) identifying (150) at least one of the plurality of different outcomes and treatment as a recommended outcome and associated treatment for the patient, wherein identifying comprises identification of an outcome and associated treatment that maximizes favorable results for two or more organ systems for the patient; and (v) providing (160) the recommended outcome and associated treatment for the patient to a user.

The invention provides an abdominal multi-organ medical image segmentation method, and belongs to the technical field of image processing, and the method specifically comprises the steps: obtaining a to-be-segmented medical image, and inputting the to-be-segmented medical image into a segmentation model; performing feature extraction on the medical image through an encoder to obtain feature maps of different levels; a next generation Transform module is applied to a bottleneck layer to process the deep feature map extracted by the encoder so as to fuse global semantic information and local detail features; transmitting the features of each encoder layer to the corresponding decoder layer through jump connections, embedding a grouping feature fusion module in each jump connection, and performing multi-scale fusion on the low-level features from the encoders and the high-level features from the decoders; and the decoder performs up-sampling and decoding processing on the fused feature maps of each layer step by step, reconstructs a segmentation result map with the same size as the input medical image, and outputs the segmentation result map. Through the scheme disclosed by the invention, the segmentation efficiency, accuracy and adaptability are improved.

The invention relates to a blood flow connection type multi-organ chip capable of being freely assembled and reused, the blood flow connection type multi-organ chip comprises a chip cover body, organ culture chambers, a chip matrix and a pressure-sensitive film, the chip matrix comprises a plurality of groups of organ culture units which are mutually independent, each group of organ culture units comprises at least two culture flow channels and a plurality of organ culture holes, and the culture flow channels are communicated with the pressure-sensitive film. The organ culture holes are sequentially distributed at intervals along the culture flow channel, the bottom end of each organ culture hole is communicated with the culture flow channel, each organ culture hole is used for accommodating an organ culture chamber, the culture flow channel is located at the bottom of the chip substrate, one end of the culture flow channel is communicated with the storage pool, and the other end of the culture flow channel is communicated with the connecting port; the chip cover body is arranged at the top of the chip base body and completely covers the storage pools and the organ culture holes, the pressure-sensitive film covers the bottom of the chip base body to seal the culture flow channels, and in each group of organ culture units, a circulation path is formed between at least two culture flow channels through the storage pools. The biological function of the human body is better simulated, and disease modeling and treatment strategy research are carried out.

The invention discloses a sepsis patient organ function damage early warning judgment method based on machine learning, and the method comprises the steps: obtaining the multi-modal clinical data of a sepsis patient, including vital sign time sequence data, inspection data, treatment intervention data and static patient basic information; after data standardization processing, organ function associated features are extracted through a multi-scale feature fusion strategy, and an organ-level feature set is constructed; the method comprises the following steps of: obtaining a multi-organ collaborative early warning model, inputting the multi-organ collaborative early warning model into a pre-trained multi-organ collaborative early warning model, respectively constructing an inter-organ compensatory relation map by double branches of the model, quantifying organ injury risk contribution degree, and outputting a multi-organ functional injury risk matrix by combining with attention mechanism weighted fusion; and generating an early warning result based on the risk matrix and the organ specificity early warning threshold, including the injury risk level of each organ and the dominant risk key feature identifier, monitoring data update in real time, and dynamically adjusting the interval to update the early warning result. According to the invention, the limitation of traditional single-source data and single-organ early warning is broken through, and the early warning accuracy and real-time performance are improved.

The present invention discloses a multi-organ registration method based on depth probability map and vector fusion, which mainly solves the problems of incorrect organ correspondence and easy conflict in the fusion of different deformation fields when performing multi-organ registration on abdominal CBCT and MRI images in the prior art. The implementation scheme is as follows: preprocess the multi-organ data and use Mask-RCNN to obtain the segmentation results; construct a multi-organ registration network composed of a multi-organ deformation field and probability map generation network and a VFF vector field fusion module, and define its loss function; use the training set data to train the network; input the test set data into the trained network to obtain the fused deformation field; use the deformation field to interpolate the target image to obtain the registration result. The present invention can well correspond to the organ relationship, effectively reduce the influence of each organ on the surrounding organs, improve the accuracy of multi-organ registration, and can be used for large-deformation cross-modal medical image registration to assist doctors in radiotherapy and puncture.

The invention discloses a pig organ automatic segmentation method and segmentation system based on a CT anatomical structure relationship. The method comprises the following steps: acquiring a whole-body CT scanning image of a live pig; and processing the pig whole body CT scanning image by using a pig multi-organ automatic segmentation model to obtain a pig multi-organ prediction mask, and completing automatic segmentation of the multiple organs of the pig. The live pig multi-organ automatic segmentation system comprises a live pig CT image acquisition module and a multi-organ automatic segmentation module, wherein the live pig CT image acquisition module is used for acquiring a to-be-segmented live pig CT image; the multi-organ automatic segmentation module comprises a visual state space block-based encoder-decoder architecture, a spatial link GRU module, a global organ category encoding module, a global organ category guiding module and a semi-supervised training framework. According to the method, multiple organs in the CT image of the live pig can be segmented efficiently and accurately, the problems of low segmentation speed and low precision of a traditional method are solved, and a more efficient solution is provided for medical image analysis and animal husbandry management of the pig.

The invention discloses a medical critical risk prediction method based on physical sign data monitored by a smart watch, and relates to the technical field of critical monitoring, and the method comprises the steps: constructing a multi-dimensional physical sign fusion analysis model, collecting dynamic physiological parameters in real time through the smart watch, and updating a personalized health baseline; based on the personalized health baseline, detecting a pathological fluctuation mode in the dynamic physiological parameters through a time sequence convolutional network; inputting the identified abnormal waveform features into a risk prediction model optimized by transfer learning, and calculating a coupling risk index of the multi-organ system; establishing a three-level response early warning mechanism based on the coupling risk index of the multi-organ system; and generating an intervention scheme based on the coupling risk index of the multi-organ system. Compared with the prior art, the intelligent wearable device has the advantages that the multi-organ risk conduction model is combined, the severe risk is accurately predicted, a risk identification-early warning-disposal dynamic management system is formed, and the clinical practical value of the intelligent wearable device in a pre-hospital emergency scene is effectively improved.

The invention discloses a method for evaluating human health by airborne pollutants, and belongs to the technical field of pollutant evaluation, and the method comprises the following steps: S1, simulating a real exposure environment, constructing an airborne pollutant exposure simulation system, S2, systematically monitoring multiple organ indexes, and systematically monitoring pollutants, and S3, carrying out interdisciplinary comprehensive analysis and evaluation. A research team formed by environmental science, medicine and molecular biology multidisciplinary professionals is established, key organ damage is analyzed, the influence of pollutants on human health is comprehensively evaluated, the research design is close to the real environment by simulating the real exposure environment, the whole process of the pollutants from exposure to excretion can be simulated, and the real exposure environment is simulated. A scientific basis is provided for evaluating the health risk of the pollutants, meanwhile, through the propagation path of the pollutants in the human body, multiple organ damages are connected in series, a systematic research framework is formed, and the limitation of single organ analysis in traditional research is avoided.

The invention discloses a three-dimensional medical image multi-organ reconstruction method based on context learning, and solves the problem of model domain migration in the prior art. The method comprises the following steps: acquiring a to-be-reasoned medical image and an image support set, preprocessing the to-be-reasoned medical image to obtain a preprocessed to-be-reasoned medical image, and preprocessing the image support set to obtain a preprocessed support set image; inputting the preprocessed to-be-reasoned medical image and the preprocessed support set image into the trained context multi-organ segmentation network for zero sample reasoning to obtain a plurality of processed images; performing post-processing according to the plurality of processed images to obtain a multi-organ three-dimensional reconstruction result; according to the method, multi-organ reconstruction of a downstream domain three-dimensional medical image can be completed without training by utilizing global context information provided by a support set.

The invention discloses an artificial intelligence chest multi-organ three-dimensional reconstruction method, and belongs to the technical field of medical image processing. Comprising the following steps: acquiring a plurality of preprocessed target CT images, and performing AI organ recognition and labeling on each target CT image; performing chest multi-organ segmentation model training according to the marked CT image to obtain a segmentation model for performing organ segmentation on the marked CT image, and obtaining multi-layer cross section data of each chest organ for performing three-dimensional reconstruction of a single chest organ; acquiring relative position information of each chest organ, and combining with the three-dimensional reconstruction model of the single chest organ to complete three-dimensional reconstruction of multiple chest organs to obtain an initial three-dimensional model; and converting the initial three-dimensional model into discrete point cloud data, constructing a point cloud anomaly detection network model to perform anomaly recognition on the point cloud data, and analyzing an anomaly recognition result by using a pathology basis large model to obtain a pathology result for performing corresponding marking on the initial three-dimensional model to obtain a target three-dimensional reconstruction model.

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 discloses a multi-organ registration method based on a cross-modal attention mechanism and vector fusion, and mainly solves the problem of poor multi-modal image multi-organ registration effect of CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) of the abdomen, the head and the neck in the prior art. According to the implementation scheme, the method comprises the following steps: preprocessing multi-organ multi-modal data of the abdomen and the head and neck; establishing a multi-modal information encoder composed of a plurality of cascaded down-sampling convolution blocks and a plurality of cascaded cross-modal attention mechanism modules; feature maps of different modal images are extracted, and the feature maps and original image feature maps extracted by different down-sampling convolution blocks are spliced and convolved to obtain a fused feature map; establishing a multi-branch single organ deformation graph decoder to generate a deformation graph of each organ; and a vector gradient fusion module is established to reduce conflicts during multi-organ deformation graph fusion, and cross-modal multi-organ registration is realized. According to the invention, the form and structure of each organ can be maintained, and the accuracy of multi-organ registration is improved. The system can be used by doctors for making operation plans, radiotherapy and auxiliary diagnosis.

The invention discloses a highly anatomical real multi-organ ultrasonic CT (Computed Tomography) image data set construction method. According to an original image of an organ, an artificial intelligence segmentation model is used for segmenting the organ into multiple types of tissues, proper medium parameter values are allocated, and a small-scale and anatomically real ultrasonic CT phantom data set is generated; performing fine adjustment on the basic generative artificial intelligence model by adopting an ultrasonic CT phantom data set to generate large-scale new data, filtering out unreasonable image results, and re-adjusting medium parameter values exceeding a reference range, so as to generate a large amount of diversified and physically real phantom data; simulating to obtain an output scattered wave field after the interaction of the sound wave and the phantom, completing the construction of a large-scale medium parameter-wave field data pair, and obtaining a multi-organ ultrasonic CT image data set; the ultrasonic CT data set constructed by the method has the characteristics of large scale, diversification and high anatomy authenticity, and is used for realizing a high-resolution ultrasonic CT image reconstruction task.

The invention discloses a small sample abdomen multi-organ image segmentation method based on a prototype network and cross attention. The method comprises the following steps: firstly, preprocessing an abdominal computed tomography (CT) image, and completing resampling and intensity normalization; dividing the preprocessed image into a support set and a query set, and constructing a small sample segmentation task; a support image and a query image are input into a deep learning network model, a cross attention module is introduced into a multi-layer structure of an encoder to explicitly model foreground, background and boundary regions, interaction and fusion among different level features are enhanced, and pixel-by-pixel matching and distinguishing of support prototype and query features are realized in combination with a double-branch contrast learning structure. According to the method, a cross attention mechanism is introduced into multiple layers of the encoder, the correlation and boundary expression ability between the features are effectively enhanced, the discrimination and robustness of the model under the small sample condition are further improved through a double-branch contrast learning structure, and therefore under the condition that labeling data is limited, the accuracy and robustness of the model are improved. And efficient and automatic segmentation of multiple organs of the abdomen is realized.

This invention discloses a universal organ-on-a-chip module, comprising a base plate, a lower substrate, a middle substrate, and a top plate. The lower substrate is stacked on the base plate and has through-holes forming a tissue culture chamber with the base plate. This tissue culture chamber is used to culture micro-tissues to simulate organs. The middle substrate is stacked on top of the lower substrate, and its lower side surface is etched with horizontal microchannels. The top plate is disposed on the middle substrate and is used to press the middle and lower substrates together. A fluid interface communicating with the horizontal microchannels is provided on the top plate. The sidewall of the lower substrate has longitudinal microchannels communicating with the horizontal microchannels on the middle substrate. This invention also discloses a three-dimensional multi-organ chip composed of multiple universal organ-on-a-chip modules and its applications. The universal organ-on-a-chip module of this invention enables modularization of organ chips, making the construction of three-dimensional multi-organ chips possible.

The invention discloses a three-dimensional ultrasonic CT image reconstruction method based on a neural network agent model. The method comprises the following steps: constructing an anatomical real organ model, simulating a wave field by virtue of a numerical solver, and generating a multi-organ training data set; constructing and training a strong scattering neural operator (S2NO) based on an iterative structure of convergence Born series, so as to solve a partial differential equation with a large computational domain and high oscillation; collecting time domain wave field observation data of a to-be-detected part of a patient, and converting the time domain wave field observation data into frequency domain wave field observation data in a set frequency range through Fourier transform; using the trained S2NO as an agent model, executing a full waveform inversion algorithm, and iteratively reconstructing an ultrasonic CT image; according to the method, a three-dimensional ultrasonic CT image of an in-vivo clinical sample is reconstructed, the speed reaches about 10 times that of a traditional ultrasonic CT full-waveform inversion reconstruction algorithm, and the imaging quality similar to the standard magnetic resonance imaging (MRI) resolution is achieved.

A multi-organ imaging system including a camera lens, a stationary, multi-examination illumination unit (SMEIU), and an attachment holder is provided. An industrial camera unit (ICU) for imaging multiple organs, for example, ear, nose, throat, and skin, is housed in a camera body. The camera lens has a fixed focal length and an iris for optimizing examination and imaging of the organs. The SMEIU is integrated to the camera body and includes illuminators arranged in a geometrical configuration. The attachment holder accommodates an organ examination attachment selected for examining an organ. The illuminators, in optical communication with one or more reflective surfaces in the organ examination attachment, produce shadowless illumination during examination and imaging of each organ, without requiring replacement of the SMEIU for examining each organ. A display unit, accommodated in a display holder detachably attached to the camera body, assists in aiming the camera lens and visualizing each organ.

This invention discloses a region-based, multi-index, multi-organ medical image segmentation model evaluation system, comprising a data acquisition module, an organ region delineation module, a data preprocessing module, a medical image segmentation model training module, a medical image segmentation model testing module, and a medical image segmentation model evaluation module. This system proposes a multi-index, multi-organ medical image segmentation model evaluation method that simultaneously quantifies multi-organ segmentation results, multiple accuracy indices, and confidence estimates in a concise and unified metric. It also provides information on the model's overall performance and its clinical usability, providing a novel and intuitive standard for clinically evaluating multi-index and multi-organ medical image segmentation models.

The invention relates to a multi-modal ultrasonic data processing and report generation method and system based on retrieval enhancement, computer equipment, a storage medium and a computer program product, and the method comprises the steps that an ultrasonic image group is acquired, and the ultrasonic image group comprises at least two ultrasonic images of different organs; analyzing the ultrasonic images in the ultrasonic image group, and classifying the ultrasonic images according to organ types; obtaining a report set of each classified ultrasonic image; and finally generating a structured ultrasonic report according to the report set of each ultrasonic image. The problems that in the prior art, a large amount of irrelevant information is forcibly injected into the generation process, and serious semantic pollution is caused are solved; the technical effects that a complex multi-organ retrieval task is decomposed into a plurality of single-organ sub-tasks through organ-level retrieval space decoupling and dynamic routing mechanisms, so that the accuracy of report generation and the clinical reliability are improved are achieved.

The invention discloses a multi-organ communicating liquid core assembly. The multi-organ communicating liquid core assembly comprises an upper cover module, a transwell module and a porous cell culture base, at least one communicated perfusion channel is arranged at the bottom in the porous cell culture base, and the communicated perfusion channel is at least communicated with one culture hole; the transwell module comprises a plurality of small transwell chambers, the small transwell chambers are matched with the culture holes, the small transwell chambers are placed on the culture holes, and the upper cover module is used for sealing and covering the porous cell culture base. According to the liquid core assembly, a traditional culture plate and a micro-fluidic technology are combined, organoid high-throughput culture and multi-organ series co-culture are achieved, a series multi-organ culture mode is adopted, a dynamic culture liquid core assembly model is established in a simple and convenient mode, and the culture efficiency is improved. The bionic degree and the uniformity of the multi-organ series co-culture tissue are improved.

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 relates to the technical field of forensic appraisal, and provides a method and system for constructing a drowning appraisal model based on virtual anatomy, and the method comprises the steps: obtaining image data corresponding to a plurality of corpses obtained through scanning the plurality of corpses based on different CT scanning devices; segmenting each piece of image data by using image processing equipment to obtain a VOI mask file corresponding to each corpse output by the image processing equipment; extracting original image omics characteristics in the VOI mask file; screening specific radiomics characteristics meeting screening conditions in the original radiomics characteristics; and constructing a multi-organ joint identification model based on a training set and a test set generated by the specific radiomics characteristics. According to the scheme, the phenomenon that the identification result is inaccurate due to the influence of external subjective and objective factors can be avoided, and the accuracy of drowning identification is improved.

The invention discloses an abdominal surgery image data analysis method and system, and relates to the technical field of data analysis of reinforcement learning, and the method comprises the steps: carrying out the feature classification of abdominal surgery image data, and obtaining an independent part representing each organ; carrying out feature decomposition on the abdominal surgery image data in each independent part through reinforcement learning, and constructing an association network between the independent parts according to a feature decomposition result; integrating the associated network into the knowledge graph between the independent parts to obtain a featured knowledge graph; in the featured knowledge graph, triggering a feature reaction through a variation mechanism; and performing data analysis according to the feature reaction to generate a final analysis result. According to the method, the analysis precision of the multi-organ image data is improved, the capabilities of feature decoupling, variation response modeling and focus recognition are achieved, and the clinical interpretability and application effectiveness of the analysis process are enhanced.

The invention discloses a multi-organ medical image segmentation method and device based on weak supervision, relates to the technical field of image processing, and mainly aims to solve the problem that existing multi-organ medical image segmentation is poor in accuracy and effectiveness. Comprising the following steps: acquiring a collected multi-organ medical image, wherein the multi-organ medical image is obtained by computed tomography; performing standardized preprocessing on the multi-organ medical image; performing image segmentation on the multi-organ medical image subjected to standardization preprocessing based on a multi-organ segmentation network model subjected to model training to obtain a multi-organ image segmentation result; wherein the multi-organ segmentation network model is constructed by a double-branch network structure, the double-branch network structure comprises a shared encoder and two parallel decoding branches, and an overall loss function of the multi-organ segmentation network model comprises a cross entropy loss function configured with a dynamic threshold.