93 results about "Histology type" patented technology

The invention provides a surgical process monitoring method and system based on machine vision, and the method comprises the steps: firstly obtaining a surgical scene real-time image sequence containing the dynamic images of a surgical instrument operation region and a biological tissue region; secondly, extracting a surgical instrument identification result (including an instrument type and a space posture) and a biological tissue segmentation result (including a tissue type and a region boundary), then monitoring an instrument operation track and a biological tissue region deformation parameter based on the results, generating a dynamic operation state parameter, and according to a predefined surgical stage safety threshold value, determining a biological tissue region deformation parameter; multi-modal anomaly detection is carried out on the dynamic operation state parameters, an abnormal event type and a risk level are determined, finally, a visual operation monitoring report is generated according to the abnormal event type and the risk level, the report covers abnormal area marks, risk prompt information and correction suggestions, comprehensive intelligent monitoring of the operation process is achieved, and the operation safety and the success rate are improved in an auxiliary mode.

Surgical marker systems and methods for delineating a lesion margin of a subject are provided. An example system includes a handheld probe device configured to capture an optical coherence tomography (OCT) image and a processor coupled to a memory. The handheld probe device includes a handheld probe including a fiber-optic probe assembly and a marker assembly. The processor is configured to: segment, by a neural network, each pixels of the OCT into different tissue-type categories; generate one or more feature vectors based at least in part on the segmented pixels; determine, by a one-class classifier, a boundary location in the OCT image between a normal tissue and an abnormal tissue of the tissue structure; and control the marker assembly to selectively create a visible label on a tissue location of the subject, the tissue location corresponding to the boundary location.

The invention provides a data enhancement method and system based on a pathological full-slice image and a storage medium, and the method comprises the steps: segmenting an obtained pathological full-slice image WSI into a plurality of image blocks, extracting a WSI representation from the WSI, and extracting an image block representation from each image block; obtaining condition input of a pre-trained first condition diffusion model based on the extracted WSI representation, inputting the noise image into the pre-trained first condition diffusion model, and outputting to obtain predicted noise WSI representation and predicted noise tissue type distribution; determining a tissue type contained in the predicted noise tissue type distribution, obtaining condition input of a pre-trained second condition diffusion model based on a category embedding vector corresponding to the determined tissue type and predicted noise WSI representation, and respectively inputting each image block representation into the pre-trained second condition diffusion model, and outputting to obtain a corresponding prediction noise image block representation. The survival prediction accuracy can be improved under the condition that the WSI number of the patient is insufficient.

The invention provides a cancer survival prediction method based on a pathological image, and relates to the technical field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, dividing an acquired pathological image to obtain a plurality of image blocks; and obtaining the feature vector of each image block and identifying the tissue type to which each image block belongs. Calculating spatial proximity, feature similarity and tissue type compatibility between the image blocks according to the center coordinates of the image blocks, the feature vectors and the tissue type to which each image block belongs; and based on a calculation result, constructing a dynamic heterogeneous graph, and performing feature extraction to obtain a multi-scale feature graph of the dynamic heterogeneous graph. And performing multi-prototype learning on the basis of the belonging organization type of the image block and a cross-category attention mechanism to generate multiple prototypes. And finally, the multi-scale feature map and the multiple prototypes are aggregated to obtain final image representation, and cancer survival prediction is performed based on the final image representation, so that the prediction accuracy of the cancer survival state is improved.

According to the invention there is provided a method of estimating an amount of at least one biomarker for a subject, based on an image of the subject imaged by a medical imaging method, the method comprising: processing the image to identify at least one tissue type within a region of interest of the image and determining an amount of each identified tissue type; associating each identified tissue type with a measure of biomarker production; calculating a total amount of biomarker production for the identified tissue types.

The invention provides a special endoscope thyroid nerve monitoring bipolar electrode, and relates to the technical field of medical apparatuses and instruments. A radio frequency output electrode is arranged in a functional electrode unit and used for applying high-frequency current to carry out tissue cutting and blood coagulation, and a nerve stimulation electrode is used for applying low-frequency current to carry out nerve stimulation and monitoring. The two electrodes work cooperatively, the impedance detection electrode can collect a voltage signal after the low-frequency current passes through the tissue, and the impedance modeling unit converts the collected voltage signal to obtain an impedance value. The position positioning module determines the position of a working point between the two electrode ends. In the operation process, the initial impedance value of the working point at the current position serves as the type impedance to judge the tissue type of the working point, tissue cutting is conducted along with a radio frequency output electrode, the impedance value change of the working point at the current position is monitored, and the tissue cutting result is judged in combination with the previously determined tissue type. And the safety and the overall efficiency of the surgical operation are improved.

A tracking system and a method for 3D tracking of a medical device, equipped of at least one ultrasound sensor, inside an anatomical region of a subject having at least one layer of a first tissue type at least partially surrounding a volume having at least one second tissue type, wherein the properties of ultrasounds propagation in the first and second tissue type are different.

The invention discloses a low-dose CT (Computed Tomography) imaging method and equipment based on semantic priori knowledge guidance. In order to solve the problems of excessive smoothness of images, detail loss and the like in low-dose CT imaging, semantic prior information is extracted through a pre-trained visual model by utilizing significant differences of different anatomical structures (such as bones, soft tissues, gas regions and the like) on noise levels, so that different tissue types are effectively distinguished, and noise modes of specific regions are captured. Meanwhile, a semantic perception module is provided, detail features concerned by the noise reduction network and high-level anatomical semantic features captured by the semantic network are aligned and fused, and it is ensured that the anatomical structure is perceived more accurately in the denoising process. Further, a segmentation network is introduced behind the noise reduction network, and a multi-task learning strategy is adopted to realize joint optimization of noise reduction and semantic segmentation. According to the design, the denoising performance of the image is improved, key anatomical details are effectively reserved, and clearer image support is provided for clinical diagnosis.

The invention discloses a postoperative pathology assessment method, survival prediction method and system for esophageal cell carcinoma, and the method comprises the steps: obtaining and preprocessing WSI image data, obtaining cut small images of the WSI image data in a low-magnification view angle, a medium-magnification view angle and a high-magnification view angle, and sequentially inputting the cut small images into a selective spatial state model through feature extraction; updating the diagnosis states through a long and short term memory network, and adjusting the contribution degree of each diagnosis state through a selective memory weight; fusing all diagnosis states, and outputting the probability of tissue types through a full connection layer and a Softmax function; calculating the percentage of the residual living tumor, and obtaining a pathological assessment result according to the percentage of the residual living tumor. According to the method, the diagnosis process of doctors can be simulated, the workload of pathologists can be reduced, the evaluation consistency and universality are improved, and the survival rate can be predicted and clinical treatment decision making can be assisted through the constructed survival prediction model.

Example systems and methods for generating virtual immunofluorescence stains for tissue samples are provided. A computing device receives a slide image of a target tissue sample of a particular tissue type. The computing device selects a first trained machine learning (ML) model to generate virtual immunofluorescence (IF) stains of a first type for the particular tissue type based on a user input. The first trained ML model is trained at least based on a first set of stain images of a plurality of training tissue samples with stains of the first type. The computing device generate a virtually stained image of the target tissue sample with the virtual IF stains of the first type using the first trained ML model. The computing device displays the virtually stained image of the target tissue sample with the virtual IF stains of the first type.

A method is provided for precision dosing of electrical stimulation of the brain. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of an electro-stimulation device including a plurality of electrodes positioned on a head of a subject. The method also includes obtaining measurements that indicate a tissue type at each voxel inside the head of the subject based on an imaging device. The method also includes determining, with a processor, a value of one or more parameters of the electro-stimulation device based on the tissue type measurements at each voxel such that the electro-stimulation device is configured to generate a value of one or more parameters of an electric field at each voxel inside the head of the subject to improve the treatment outcome of the subject.

Systems and methods for ablating target tissue, measuring parameters during ablation such as temperature of the target tissue, and predicting volume of the ablation based on the measured parameters are provided. The system may include a switching antenna for both heating of target tissue and radiometry to monitor the temperature of the heated tissue, and a processor for calculating the temperature of the target tissue, segmenting medical images, and predicting volume of the ablation based on radiometric signals indicative of the target tissue temperature. The predicted ablation volume may be adapted to account for tissue boundaries and anatomical structures. The processor further may determine properties of the target tissue such as tissue type.

The application relates to the technical field of medical data processing, and discloses a macroscopic cross-section database construction method, a dose determination method, a device and equipment, which comprises the following steps: setting multiple boron concentration values for the tissue types corresponding to a human body model; selecting corresponding neutron source parameters for the tissue types and the multiple boron concentration values, wherein the neutron source parameters comprise energy range values; dividing the energy range values into multiple discrete neutron energy groups; performing particle transport simulation based on the selected neutron source parameters, jumping between different neutron energy groups in the simulation process, and obtaining neutron reaction rates and neutron fluxes of the tissue types under the multiple boron concentration values and the multiple neutron energy groups; and obtaining neutron macroscopic cross-section values of the tissue types under the multiple boron concentration values and the multiple neutron energy groups based on the neutron reaction rates and the neutron fluxes, and constructing a multi-group macroscopic cross-section database. The application can improve the calculation efficiency of the radiation dose and improve the adaptability to individual metabolic differences during dose calculation.

Systems and methods for ablating target tissue, measuring parameters during ablation such as temperature of the target tissue, and predicting volume of the ablation based on the measured parameters are provided. The system may include a switching antenna for both heating of target tissue and radiometry to monitor the temperature of the heated tissue, and a processor for calculating the temperature of the target tissue, segmenting medical images, and predicting volume of the ablation based on radiometric signals indicative of the target tissue temperature. The predicted ablation volume may be adapted to account for tissue boundaries and anatomical structures. The processor further may determine properties of the target tissue such as tissue type.

Techniques and systems for using spectroscopy, and / or image-based sensing with an intravascular blood pump are provided. The techniques generally include capturing a spectral response to light of a measured tissue and / or blood. Based on the specific needs, the method may include, e.g., determining a type of the measured tissue based on the spectral response, determining if the type of the measured tissue matches a target tissue type, determining a health of the measured tissue based on the spectral response, determining a treatment plan based on the health of the measured tissue, determining a type and / or quantity of components and / or characteristics in the blood, and / or determining a degree of heart recovery based on the type and / or quantity of the components and / or characteristics in the blood.

The invention is suitable for the technical field of spinal endoscopes, and provides a multi-wavelength AI control device, method and system for minimally invasive spine surgery, and the device comprises a light source module which is used for generating a plurality of irradiation light rays; the interior of the working pipeline is hollow, and the working pipeline is used for arranging an optical fiber imaging module in a penetrating mode and providing illumination for an imaging lens of the optical fiber imaging module; the AI analysis module is used for performing AI real-time analysis on the imaging image acquired by the optical fiber imaging module to obtain a tissue type in the imaging image; a plurality of light source control schemes are preset in the logic control module. Compared with the prior art, the method has the advantages that by means of the three-in-one method of multi-wavelength optical imaging, AI real-time analysis and self-adaptive light source control, the tissue identification precision, the operation view imaging stability and the operation safety of the device are remarkably improved, and the method is particularly suitable for complex scenes needing high-precision dissection and separation.

The present application relates to the technical field of vascular interventional treatment instruments, and discloses a system and method for the selective laser ablation of restenosis after the treatment of Budd-Chiari syndrome, comprising a controller, a pulse emission module and a reference element built into a catheter, the system is based on the reference element for periodic self-calibration, collects and processes the associated photoacoustic signals to identify the tissue type, and then realizes the selective ablation of the diseased tissue and the boundary self-stop when the healthy tissue is touched. The present application converts the traditional open-loop energy delivery into a closed-loop self-control process with real-time information interaction capability, so that the ablation operation end can in-situ perceive the tissue boundary and realize autonomous stop, thereby avoiding the problem of damaging the healthy tissue due to uncontrollable energy application, improving the safety of the operation, and having the advantages of high precision, few complications, repeatable treatment and the like.

The invention relates to a laser therapeutic instrument, a laser therapeutic instrument operation control device and a storage medium. The laser therapeutic instrument comprises a processor and a memory. Wherein the memory is used for storing computer programs; the processor executes the following steps when running a computer program: acquiring static characteristics and dynamic characteristics of a target object; based on the static features and the dynamic features, combining a tissue characteristic map and a prediction model to obtain a change trend prediction result of the target object; the tissue characteristic map comprises static characteristic reference values of different tissue types / skin types and a historical change rule curve of dynamic characteristics along with time; the prediction model is obtained based on constitutive equation training of the target object; on the basis of the variation trend prediction result, working parameters during operation on each region of the target object are determined in combination with an energy gradient distribution algorithm; and on the basis of the working parameters, real-time parameters of corresponding components in the laser therapeutic instrument are adjusted, and treatment of the target object is completed. Therefore, the accuracy and the stability of treatment are improved.

A method for generating a transducer layout to deliver a tumor therapy electric field includes storing medical images of a subject, identifying one of the medical images as an anchor medical image, and registering a computed tomography medical image with a magnetic resonance imaging medical image. The method further includes segmenting abnormal tissue from other tissue types in the medical image and defining a region of interest (ROI) in the medical image. The method further includes creating a 3D model of the subject. The method further includes generating a plurality of transducer layouts to apply a tumor therapy electric field to the subject, selecting at least two of the transducer layouts as recommended transducer layouts, presenting the recommended transducer layouts, receiving a user selection of at least one recommended transducer layout, and presenting the recommended transducer layouts to the subject. And providing a report for the selected and recommended transducer layout.

The invention discloses an oral cancer diagnosis model construction method, which is based on DeepLabv3 +, increases an auxiliary branch to provide position information for a diagnosis model and increases SCConv attention so as to realize segmentation processing of oral cancer multi-tissue category pathological images. The method comprises the following steps: acquiring sample set labeling data sets ORA-TCGA and ORA-Nine for training a diagnosis model, classifying data set tissues into tumors, epithelium, interstitial substances, lymphatic vessels, skeletal muscles and vessels, and performing diagnosis model training and prediction by using OCMS-Net. The diagnosis model is based on OCMS-Net and comprises a main branch A and an auxiliary branch B, the input of the main branch A is an oral cancer pathological image, the output of the main branch A is a segmentation mask, and the segmentation mask represents different tissue types with different colors; the input of the auxiliary branch B is an oral cancer pathological image, and the output of the auxiliary branch B is a binary segmentation map and a nuclear pixel distance map of a cell nucleus.

The invention relates to the technical field of vascular interventional therapy instruments, and discloses an excimer laser ablation system and method for restenosis after Budd-Chiari syndrome interventional therapy, the excimer laser ablation system comprises a controller, a pulse transmitting module and a reference element arranged in a catheter, and the system performs periodic self-calibration based on the reference element; according to the method, associated photoacoustic signals are collected and processed to identify tissue types, selective ablation of lesion tissues and boundary self-stop when healthy tissues are touched are further achieved, traditional open-loop energy delivery is converted into a closed-loop self-control process with real-time information interaction capacity, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The ablation operation tail end can sense the tissue boundary in situ and achieve automatic stopping, so that the problem that healthy tissue is damaged due to uncontrollable energy application is solved, the safety of an operation is improved, and the advantages of being high in precision, few in complication, capable of achieving repeated treatment and the like are achieved.

The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation (26) emitted by a tissue sample (24) of the tissue is sensed (10) by means of a radiation sensor (22), the radiation sensor (22) providing a sensor signal (28) in accordance with the sensed electromagnetic radiation, and the sensor signal (28) is evaluated (12) by means of an evaluation unit (30) in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit (30) is a self-learning evaluation unit (30) that is initially trained (14) by means of training data sets (32) on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets (32) each comprising a training sensor signal with an associated training tissue type.

Methods and systems for characterizing a biological structure including: obtaining image data for the biological structure using an imaging system; constructing a volumetric dataset of the biological structure based on the image data; determining at least one of a plurality of sections along a plane or curve within the volumetric dataset; and characterizing, using a trained algorithm and based on at least one of the plurality of sections, the biological structure for potential tissue type or disease state.

In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for training one or more models to predict ulcerative colitis (UC) severity based on human-interpretable image features extracted from a whole-slide image, including acts of accessing a plurality of annotated whole-slide images associated with a plurality of UC patients, wherein each of the plurality of annotated whole-slide images includes at least one annotation describing a cell-type label or a tissue-type segmentation for a portion of the whole-slide image, extracting a plurality of human-interpretable image features based on cell-type labels and tissue-type segmentations associated with the plurality of annotated whole-slide images, training a statistical model based on the plurality of human-interpretable image features to predict the UC severity for a whole-slide image, and storing the trained model on at least one storage device.

The invention relates to an artificial intelligence-based abdominal cavity operation auxiliary control method, device and equipment and a medium. The method comprises the following steps: acquiring kinematics data of the intelligent surgical instrument and physical data in contact with tissues; geometric-physical fusion correction is carried out on the input pressure to obtain effective clamping pressure, and a contact quality index is calculated; extracting multi-dimensional electrical characteristics and obtaining tissue type labels through an AI tissue classifier; a target working state is generated through verification of the neural finite-state machine; and during plasma cutting, the residual life of the liquid film is predicted through LSTM, and a corresponding control signal is generated. By adopting the method, the accuracy, the safety and the stability of auxiliary control of the abdominal surgery can be improved, and reliable support is provided for surgery development.

The invention discloses a duodenum mucosa remodeling system based on pulse field ablation and a control method, and the system comprises a multi-modal sensing ablation catheter which is used for collecting multi-modal data related to duodenum ablation through a multi-modal sensor; the multi-sensing data fusion unit is used for extracting key features after preprocessing the multi-modal data, and fusing the key features according to a preset algorithm to obtain a multi-modal feature vector; the real-time tissue feature analysis module is used for recognizing the tissue type of the current ablation point location in real time according to the multi-modal feature vector and analyzing the dynamic change of tissue features; the self-adaptive parameter adjustment engine is used for determining corresponding ablation parameters according to the tissue type of the current ablation point location and the dynamic change of the tissue characteristics; and the motion compensation and prediction system is used for predicting the motion trail of the duodenum in a future preset time period. The problem that in the prior art, a duodenum mucosa remodeling system is poor in remodeling effect is solved.

A multispectral photonic imaging system and associated methods are disclosed for classifying wound tissue and generating customized therapeutic wound dressings. The system employs wavelength-specific optical sources and integrated image sensors to capture multispectral reflectance data from a wound, enabling computational classification of distinct tissue types such as granulation, slough, and necrosis. A three-dimensional (3D) digital model of the wound is constructed using spatial data from depth sensors. The classified 3D wound model facilitates automated design of patient-specific dressings, featuring heterogeneous regions tailored with therapeutic compounds like antimicrobial agents, analgesics, collagen scaffolds, and hydrogel matrices precisely aligned to corresponding tissue classifications. Additional imaging modalities, including thermal and ultrasound sensors, can be integrated to enhance classification accuracy and inform targeted therapeutic placement, providing a comprehensive and personalized approach to wound management for medical and veterinary applications.

A laser or ultrasonic instrument is used to remove tissue during a surgery, such as to form one or more pilot holes in a vertebra or a window in bone. Where a laser is used, interrogative laser pulses can be used to obtain information, such as detecting depth or tissue type.

Provided is a pathological diagnosis assistance method in which a microscopic observation image data is acquired by specifying a position, the image data is classified by tissue type using AI, the classification result is integrated as a lesion site, and thus the diagnosis is assisted. Provided is a pathological diagnosis assistance method that can provide an assistance technology for performing pathological diagnosis with high precision and high efficiency by HE staining, which is commonly used by pathologists. Further, a pathological diagnosis assistance system, a pathological diagnosis assistance program, and a learned model are provided.

A surgical instrument includes an end effector configured to clamp tissue, a motor configured to actuate the end effector, and a controller in communication with the motor and configured to determine a stress and strain of the tissue, identify a tissue type of the tissue based on the determined stress and strain of the tissue, and set an operational parameter of the surgical instrument based on the identified tissue type of the tissue.