100 results about "DICOM" patented technology

The invention discloses a medical image optimization method and system based on vascular branch selective blurring. The method comprises the following steps: carrying out preprocessing and blood vessel enhancement on a three-dimensional angiography DICOM image; performing precise blood vessel segmentation by using an encoder-decoder network comprising a wide activation and residual cavity space pyramid module; identifying an interference branch which shields the target blood vessel structure at a specific working angle through blood vessel topology analysis; selective blurring repair is performed on the interference branches based on a Poisson equation and a bidirectional convolution LSTM to generate an optimized image which is visually unobstructed and keeps topological continuity. And a safety mechanism of virtual-real combined display and multi-angle plan planning is introduced, so that the reliability of surgical navigation is ensured. And finally, integrating the optimized 3D blood vessel model to a radiography system supporting real-time synchronization, and using the 3D blood vessel model as a road map. The clinical problem that the optimal working angle cannot be used due to blood vessel shielding can be effectively solved, and the precision and safety of an endovascular interventional operation are remarkably improved.

The invention relates to the technical field of medical image processing, in particular to a DICOM sequence intelligent normalization and sorting method and system, and the method comprises the steps: obtaining an original DICOM file; analyzing a key DICOM tag of the original DICOM file to obtain a direction tag and body position information, and based on the direction tag, realizing normalization based on a patient coordinate system and performing direction correction; projecting the coordinates of all the slices to a normal vector direction to reconstruct a layer sequence, and counting a slice interlayer spacing sequence to estimate a real interlayer spacing based on adjacent projection difference values; identifying and rejecting abnormal slices based on the abnormal comparison target label and the slice interlayer spacing sequence; and for a multi-sequence set of the same check, extracting a sorting feature vector corresponding to each sequence, and selecting a sorting rule in combination with scene complexity to obtain a final sorting key for representing a sorting result of the phase dimension and / or the time dimension. The method has the effect of solving the problems that existing DICOM sequences are inconsistent in direction, disordered in layer sequence, difficult in time phase recognition and the like.

The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

The application provides a lung CT image processing method and system based on multi-scale space semantic fusion, which comprises the following steps: collecting multiple lung CT-DICOM images, and performing quality inspection processing and format conversion to obtain original sample images; pre-processing and lung parenchyma segmentation are performed on the original sample images to obtain standard sample images; positive and negative samples are determined according to the image labeling results, and the positive and negative samples are subjected to random interference to obtain expanded sample images; a training data set is generated based on the expanded sample images and the standard sample images; the YOLOv12 network is improved based on the MSSF module and the C3K2_MSSF module to obtain an improved YOLOv12-3D network, and the improved YOLOv12-3D network is trained by using the training data set to obtain a target detection model; and the CT image to be detected is input into the target detection model to obtain detection information. The improved YOLOv12-3D network architecture realizes deep fusion of three-dimensional space features and multi-scale space semantic features in the feature extraction stage, and significantly improves the extraction rate and efficiency of small targets.

The present application relates to the technical field of image segmentation, and provides a lung image segmentation method, which comprises the following steps: S1: inputting a lung CT dicom sequence and converting the original dicom sequence into a jpg format image as an original lung image; S2: enhancing the original lung image through an image enhancement Lap-CLAHE algorithm to obtain an enhanced image; and S3: inputting the enhanced image into a U-shaped symmetrical network segmentation model 3D-SDUnet composed of an encoder, a decoder, a DPA double-path attention and a skip connection, and outputting a final segmentation result of the lung image. According to the technical scheme, the lung image is first enhanced, the detail information is improved, and the good image contrast is maintained, so that high-quality input data is provided for subsequent model segmentation. The segmentation model is optimized, the skip connection composed of a dense Swin Transformer block (DATB) and the DPA attention module are added, the information connection between the encoder and the decoder is strengthened, the segmentation precision is improved, and a complete lung model is obtained.

The application provides an image segmentation method and device, the image segmentation method comprises the following steps: obtaining a DICOM image to be segmented; performing feature extraction on the DICOM image based on a multi-layer perception machine, obtaining a plurality of sampling feature maps, and performing convolution operation on a target sampling feature map to obtain a multi-scale feature map; and obtaining a segmentation image based on the sampling feature map with the highest image resolution and the multi-scale feature map. The method can obtain the width information and height information of the image by obtaining a plurality of sampling feature maps of the DICOM image through the multi-layer perception machine, and can obtain the abstract information of the image by performing convolution operation on the target sampling feature map to obtain the multi-scale feature map. Finally, the sampling feature map with the highest image resolution and the multi-scale feature map are fused to realize the splicing of the feature information of different dimensions of the image, and the segmentation image is obtained according to the fused feature map, thereby improving the image segmentation accuracy.

Provided are an apparatus and method for verifying forgery of a medical image using a hash value based on meta information of the medical image.SOLUTION: The forgery verification apparatus 400 includes a memory configured to store patient-specific DICOM files and a processor functionally connected to the memory, and the processor is configured to, when issuance of a DICOM file is requested, search for the requested DICOM file among the patient-specific DICOM files, generate an issuance number associated with the issuance, extract a part of meta information of the DICOM file, generate a first hash value using the part of the meta information and the issuance number, and store the first hash value in the memory in association with the issuance number. While providing a copy of a DICOM file to an external storage device, a first hash value is inserted into a designated tag area of the copy as forgery verification information.SELECTED DRAWING: Figure 1

Disclosed herein are methods for selecting and preparing patient data to facilitate the adoption and customized training of machine-learning models in clinical settings, particularly for radiation therapy treatment planning. The disclosed embodiments streamline the customized training process through an automated workflow that includes prefiltering patient metadata, retrieving relevant DICOM files, optional data anonymization, and generation of training data. The data is then organized into a format suitable for machine-learning training. The embodiments discussed herein reduce manual labor, minimize errors, and accelerate the integration of machine-learning into clinical workflows, enabling clinics to train and implement predictive models that replicate specific clinical practices, thereby enhancing treatment precision and improving patient outcomes.

The invention discloses a display characteristic curve correction method and system for a medical LED display screen. The method comprises the first step of full-gray-scale range brightness data collection, the second step of pixel-level response curve fitting, the third step of medical standard parameter calculation, the fourth step of real-time dynamic correction application and the fifth step of intelligent periodic maintenance management. According to the invention, through point-by-point correction and curve fitting in the whole gray scale range, high-precision display characteristic compensation is realized, and the uniformity and stability of the display screen are ensured; medical standards such as DICOM, GSDF and the like are strictly followed, and the reliability and diagnosis accuracy of medical image display are improved; the automatic periodic correction mechanism reduces manual intervention, improves the maintenance efficiency and prolongs the service life of the display screen.

This application discloses a method and related products for training a generative model for generating three-dimensional aneurysms. The method includes: extracting at least one perspective of two-dimensional image data from original three-dimensional image data; semi-transparentizing the aneurysm-bearing artery region in the two-dimensional image data and marking the centerline of the aneurysm-bearing artery to obtain preprocessed data; extracting aneurysm-related two-dimensional and three-dimensional features using a pre-trained image encoder and shape encoder based on the preprocessed data and the original three-dimensional image data; and training the generative model using the two-dimensional and three-dimensional features as the input and output, respectively, so that the generative model outputs a three-dimensional aneurysm containing the aneurysm-bearing artery region and surrounding vessels. Using the solution of this application, high-precision three-dimensional aneurysm reconstruction can be achieved without original DICOM data, providing a reliable aneurysm-assisted diagnosis and treatment decision-making solution.

The method of obtaining DICOM files may comprise receiving, by a processor, scanning data of a scannable graphic associated with an item, wherein the scannable graphic is associated with DICOM tag data; extracting, by the processor, the DICOM tag data from the scanning data; determining, by the processor, that the DICOM tag data is associated with a first DICOM file; identifying, by the processor, a user of the first DICOM file; retrieving, by the processor, the first DICOM file; searching, by the processor, for other DICOM files that are associated with at least one of the user or the first DICOM file; retrieving, by the processor, the other DICOM files; and sending, by the processor and to the user, the first DICOM file and the other DICOM files.

The invention discloses a three-dimensional surgical risk model reconstruction method based on brain anatomy and a functional atlas, and the method comprises the steps: S1, carrying out the image processing and fine segmentation of a brain MRI image or a DICOM sequence image of a patient, and recognizing a plurality of anatomical structures; s2, establishing a multi-dimensional risk assessment model, and performing quantitative scoring on each brain region from four dimensions of functional importance, structural vulnerability, recovery potential and clinical significance; s3, constructing a continuous three-dimensional risk field based on the risk score, and realizing the propagation of the risk value in the brain tissue based on a Gaussian diffusion model; and S4, based on an improved path planning algorithm, searching an optimal operation path in the three-dimensional risk field to realize risk minimization. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The application discloses a kind of semi-automatic bootstrap medical image sequence marking method, comprising the following steps: facing single frame or multiple frames DICOM and its derived sequence, frame analysis and export lossless image sequence, establish sequence index to support hierarchical batch access, sampling and cross-frame operation, estimate imaging window (sound window) and crop by interframe pixel variation statistics, support manual frame selection, reuse history crop frame and skip abnormal sequence, when marking, based on prompt point set and neighborhood brightness distribution adaptive threshold determination, connected extraction generates effusion target mask;Magnetic tracing is provided to highlight boundary and bone surface boundary is generated along texture fitting, optionally reuse last frame prompt point / mask or optical flow propagation to subsequent frame, and support cross-frame interpolation, and can be integrated segmentation model automatic marking, correction and incremental training to form closed loop;Mask is saved with file tail additional lossless compression section, supports cover update, does not affect general reading and can be restored.

The application relates to the technical field of data transmission control, and discloses a DICOM image routing method and system based on port multiplexing. By binding a shared DICOM port and listening to a connection request, multiple backend DICOM receiving nodes can share a unified access port, a command set and a data set are selectively parsed according to a command type, a structured session context is constructed, routing judgment is upgraded from a traditional coarse-grained mode based on a source IP and a source port to a fine-grained mode based on protocol semantics, a routing decision module adopts a multi-field rule matching and index acceleration mechanism, can improve rule screening efficiency and reduce matching delay in a scene with more concurrent connections, and a routing optimization module dynamically evaluates in combination with running state information and transmission performance information of candidate target DICOM receiving nodes, so that a communication bottleneck can be avoided, in which a fixed transmission path is long-term concentrated on part of nodes.

This invention discloses a method for evaluating the scanning depth of breast ABUS images, belonging to the field of breast ABUS image technology. The method includes: S1: collecting three-dimensional DICOM data of breast ultrasound, annotating the ribs in the coronal images, and evaluating the scanning depth; S2: performing coronal slice processing and converting and generating model labels; S3: detecting the rib region based on a target detection architecture, and training a rib detection model capable of automatically locating the rib region in the image; S4: testing and evaluating the rib detection model; S5: establishing scanning depth judgment rules based on the number and position of rib frames to determine the scanning depth category. This invention solves the problems of significant differences in image coverage, lack of reliable reference, and difficulty in quickly determining the optimal scanning depth suitable for different breast sizes and shapes. This invention's automatic evaluation of ABUS image scanning depth enables standardization of ultrasound screening.

The invention discloses a private library local area network medical knowledge security sharing system, and belongs to the technical field of medical health information. The system takes a multi-modal medical knowledge analysis and reconstruction engine as a core, accesses multi-source heterogeneous medical data through interfaces such as DICOM and HL7, and performs semantic analysis and standardized packaging based on a medical ontology knowledge graph to generate a unified medical knowledge object. The system constructs a controlled peer-to-peer private library network, and realizes two-way encryption transmission and fine-grained dynamic access control between nodes based on a digital certificate; classification and desensitization are carried out on the recognizable information of the patient through the content security gateway, and invisible watermarks are embedded to realize knowledge circulation full-life-cycle traceability. Unified semantic analysis and clinical decision rule conversion of multi-source medical knowledge are realized, and the medical knowledge integration efficiency and sharing compliance in a local area network are remarkably improved on the premise of ensuring privacy security.

The invention discloses a kidney stone, hydronephrosis and pyosis detection system based on multi-scale feature fusion, and belongs to the technical field of medical detection, and the working process of the detection system comprises the following steps: S1, image data acquisition and preprocessing; s2, feature extraction and multi-scale feature fusion of the model; s3, classification decision and output layer design; and S4, model training and optimization. According to the system, from original DICOM image input to final diagnosis report generation, full-process automatic processing is achieved, manual intervention on feature engineering or intermediate steps is not needed, the system can generate a structured diagnosis report which comprises specific disease classification, confidence score and visual evidence heat map, the output format is normative, and the diagnosis report can be used for diagnosis. The method is easy to integrate with an existing image archiving and communication system of a hospital, and the model is clearly guided to pay attention to specific features related to ponding and infection in the learning process by introducing independent ponding and infection auxiliary discrimination branches and performing joint optimization with a main classification task.

The invention discloses an ultra-short echo sequence magnetic resonance image processing method and device and a medium. The processing method comprises the following steps: performing magnetic resonance scanning on a to-be-imaged part of a patient by adopting a UTE sequence, acquiring N (N is greater than or equal to 4) magnetic resonance images of echo time, and storing the magnetic resonance images as image data in a DICOM format; converting the magnetic resonance image data from a DICOM format to an NIFTI format for storage; and performing analytic calculation on the long and short values by using a four-point analytic model based on piecewise exponential decay hypothesis, and finally obtaining a three-dimensional parameter distribution diagram of the rapid relaxation time, the slow relaxation time and the proportion (f) of rapid relaxation time protons in the ultra-short echo signal. According to the method, the processing time of the three-dimensional UTE image data is remarkably shortened to about 10 seconds from 10 hours of a traditional method by adopting an analytical algorithm, and meanwhile, the proportion of the number of slow relaxation protons and the number of fast relaxation protons can be effectively distinguished.

Embodiments of the present disclosure relate to customized machine learning training of radiotherapy clinics. Disclosed herein are methods for selecting and preparing patient data to facilitate the employment and custom training of machine learning models in a clinical environment, particularly for radiotherapy treatment planning. The disclosed embodiments simplify the customized training process through an automated workflow that includes pre-filtering patient metadata, retrieving related DICOM files, optional data anonymization, and generating training data. And organizing the data into a format suitable for machine learning training. The embodiments discussed herein reduce manual labor, minimize errors, and accelerate the integration of machine learning into the clinical workflow, enabling clinics to train and implement predictive models replicating specific clinical practices, thereby improving treatment accuracy and improving patient outcomes.

The invention discloses a medical image focus automatic identification system based on deep learning, and relates to the technical field of medical image intelligent analysis. The system supports the access of CT, MRI and X-ray equipment, adapts to various image formats and carries out calibration to keep the physical significance consistent; processing image artifacts by adopting a denoising-normalization-cutting three-stage process, and unifying the size of the region of interest; a multi-branch CNN and Transform hybrid model is adopted, and based on large-scale annotation data set training, local and global features are extracted; outputting the category, size, position and malignant risk level of the focus; a structured report, DICOM (Digital Imaging and Communications in Medicine) labeling and visual display are supported; regularly updating the model by adopting a hierarchical storage architecture; monitoring a system operation index, and triggering an alarm when the index is abnormal. The image processing consistency and the focus identification accuracy are improved, the risk judgment is optimized in combination with clinical information, the data privacy and the system quality are guaranteed, and the diagnosis efficiency and reliability are high.

The medical exam pre-pushing system and method proactively distributes medical imaging data from a long-term archive system to multiple PACS. It revolutionizes how historical image data can be transferred proactively from long-term archive to PACS. The system monitors upcoming medical examinations through a hospital information system or electronic medical record interface and identifies patients requiring access to prior imaging studies. The archive system proactively transmits relevant historical imaging data to designated receiving PACS before clinical need arises. The system implements two optimized transmission methods: multi-threaded DICOM Send operations that eliminate resource-intensive query components, and multi-threaded direct file transfer using SMB / CIFS protocol that bypasses DICOM layers entirely for high-volume transfers. This proactive, archive-driven approach improves data transfer speed by a factor of double-digit or even triple-digit, eliminates duplicate query requests from multiple PACS sources, reduces network traffic, and ensures immediate availability of prior studies for clinical comparison purposes.

The invention provides a DICOM file processing method and device, equipment, a medium and a product. The method comprises the steps of obtaining a to-be-processed DICOM file, and performing anomaly detection and preprocessing on the to-be-processed DICOM file; according to the preprocessed non-abnormal DICOM file, analyzing the non-abnormal DICOM file to obtain analyzed multi-modal image data and metadata, and carrying out persistence processing on the analyzed multi-modal image data and metadata; identifying an image type of the multi-modal image data, and calling a data display template corresponding to the image type; and screening target metadata associated with the image type from the metadata after persistence processing, and filling the target metadata and the analyzed multi-modal image data into a data display template to form an image display report. The metadata of the DICOM file can be quickly obtained, and the overall efficiency of medical image data processing, image reading convenience and the standardization level of image report generation are improved.

This invention provides a method and system for DR image quality control based on decoupled representation learning and multi-level feedback, belonging to the field of medical image processing technology. The method includes: acquiring DR images and DICOM metadata; automatically classifying areas if the examination site is missing; extracting mixed features using an adaptive anatomical perception network; obtaining anatomical, quality, and pathological features through orthogonal decoupling; guiding region-specific quality assessment based on anatomical features and inversely generating suggestions for adjusting imaging parameters. This invention achieves complete separation of lesion and quality information through orthogonal feature decoupling, avoiding interference from pathological features in quality assessment, while providing actionable parameter feedback, significantly improving the accuracy, robustness, and clinical applicability of DR image quality control.

The application discloses a kind of medical image repeated detection methods based on deep learning and multimodal analysis, belong to artificial intelligence and image processing field, including: obtaining the DICOM file of the medical image to be detected and preliminary verification;To the AI detection model that has been trained, parse out target image pixel array and fusion text vector;Target image pixel array is carried out gray scale conversion and standardization processing and with fusion text vector after verification standardization image tensor is obtained, feature is extracted to obtain target feature vector;Retrieve its multiple nearest neighbor historical feature vectors and associated historical DICOM file data;Based on the obtained fusion text vector, calculate the difference between the medical image to be detected and each historical medical image retrieved in feature vector dimension, image pixel dimension and multimodal check dimension, obtain similarity score, determine that there is repeated anomaly when exceeding threshold value and alarm, output detection result.The application can improve the accuracy of medical image repeated detection.

The application discloses a regional center AI scheduling management method, comprising: setting a business emergency degree for each image examination type, and setting a business calculation level for each image AI model; wherein each image examination type corresponds to an image AI model matched therewith; the business emergency degree is one of emergency, sub-urgency and non-urgency; and the business calculation level is one of high, medium and low. A patient DICOM image is received, the image examination type of each patient is judged, each image examination type forms an image queue to be processed, the image queue to be processed is scheduled based on the business emergency degree and the business calculation level, and a scheduling scheme of the image queue to be processed is output. The application further provides a regional center AI scheduling management system. The application provides a scheduling logic for the work of a regional center AI system, so that the business performance of the center AI can meet the importance and rationality of clinical requirements in priority.

This invention discloses a fully automated quality control method for knee joint DR radiographs based on multi-task deep learning and geometric quantization. The method includes: using DICOM format knee joint DR images as input data and performing data preprocessing; constructing a multi-task model architecture comprising a knee joint anteroposterior and lateral bone segmentation model, a knee joint anteroposterior and lateral keypoint detection model, a foreign object detection model, and a left / right marker detection model, defining the network infrastructure and input / output of each model; calculating quality control-related indicators based on the bone mask, keypoints, foreign object detection results, and left / right marker detection results output by the four models; formulating scoring rules to score the quality of knee joint DR images, and performing automatic quality control of knee joint DR images. This invention achieves objective, efficient, and quantifiable knee joint DR image quality assessment by automatically extracting anatomical structures, calculating geometric parameters, and executing scoring rules, providing a reusable technical paradigm.

Provided are an apparatus and method for verifying forgery and tampering of a medical image. An apparatus for verifying forgery and tampering of a medical image according to an embodiment disclosed in this document includes a memory configured to store Digital Imaging and Communications in Medicine (DICOM) files for each patient, and a processor functionally connected to the memory, wherein, when an issuance request of a DICOM file is received, search for a DICOM file corresponding to the request among DICOM files for each patient; generate an issuance number related to the issuance; extract some information from metadata of the searched DICOM file; generate a first hash value using the some information and the issuance number; and upon providing a copy of the DICOM file to an external storage device, insert the first hash value as forgery verification information into a specified tag area of the copy.

A method, system and computer program product for anonymous and secure sharing of medical images through a messaging tool within a picture archiving and communication system (PACS), the PACS comprising medical images captured by a medical imaging apparatus, wherein the medical images include associated digital imaging and communications in medicine (DICOM) data. The method includes: displaying a messaging tool GUI including a dialog thread pane, a message input field, and an icon for capturing a snapshot of a viewer GUI displaying at least one medical image including a DICOM data overlay layer comprising protected health information (PHI) of an imaged subject; capturing the snapshot of the viewer GUI displaying the at least one medical image; generating a de-identified medical image based on the snapshot of the viewer GUI displaying the at least one medical image, wherein the de-identified medical images excludes the PHI.

The invention discloses a medical image analysis method based on deep learning, and belongs to the technical field of medical image analysis. The method comprises the following steps: automatically acquiring a medical image through a DICOM interface and carrying out standardized preprocessing; performing feature extraction and semantic matching on the preprocessed image and related patient text information by adopting a multi-modal deep learning model based on an OpenCLIP architecture to generate a preliminary analysis conclusion and confidence; matching the preliminary conclusion with a structured disease category vocabulary library, and outputting a structured analysis report containing disease category names, subcategories and confidence coefficients; and when the confidence coefficient is lower than a threshold value, automatically optimizing the disease lexical library and carrying out iterative training on the model. According to the invention, full-process automation of medical image analysis is realized, and the diagnosis efficiency, accuracy and system adaptability are effectively improved.

The invention discloses a DICOM file processing method and device, electronic equipment and a storage medium. The DICOM file processing method comprises the following steps: acquiring a DICOM file, and carrying out standardized preprocessing and compression demand detection on the DICOM file; under the condition that the DICOM file has a compression requirement, data features of the DICOM file are identified, and the data features comprise at least one of modal features, window positions, bit depths, image frame numbers and pixel-level features of the image data; determining a compression strategy adaptive to the DICOM file based on the data characteristics of the DICOM file; according to the method, the data content in the DICOM file is compressed based on the compression strategy to obtain the compressed data, and the compressed data is subjected to layered packaging and encrypted transmission, so that lossless compression of the DICOM file is realized, the quality of the DICOM file is ensured, and comprehensive data support is provided for subsequent decision making.