45 results about "Tumor region" patented technology

The application discloses a three-dimensional near-infrared spectral tomography reconstruction method based on a mask prior, and belongs to the field of medical image processing. The method respectively performs multi-scale convolution coding on NIRST optical signals and MRI volume data to obtain features, then embeds structural information of a mask as a prior condition into a feature fusion module, dynamically adjusts spatial weights in the fusion process of multi-scale structural information and optical functional information, so that the network focuses on potential lesion areas and suppresses background noise, and finally reconstructs the optical parameter distribution of the tissue through a three-dimensional decoding network. The method provided by the application can effectively reduce the ill-posedness in the NIRST reconstruction problem, reduce artifact generation, improve the positioning accuracy of the tumor area and the optical property reconstruction quality, and reconstruct the optical property parameter distribution of the biological tissue.

This invention discloses an automatic tumor region annotation method for whole-section pathological images of colorectal cancer, belonging to the field of pathological image processing technology. It employs an improved Inception-V3 network, embedding a colorectal cancer feature extraction branch to extract cellular, glandular, and tissue-level morphological features associated with colorectal cancer from stained whole-section pathological images. A fused feature map is obtained by weighting the contribution coefficients of colorectal cancer gene morphology. Based on the colorectal cancer morphological fingerprint vector, an improved Otsu algorithm is used to generate an initial segmentation threshold. The optimal segmentation threshold is obtained through iterative optimization using a colorectal cancer gene morphology matching degree formula, thus initially dividing the tumor candidate region. This invention deeply couples the molecular features of core driver mutation genes in colorectal cancer with the morphological features of pathological images, constructing a dedicated morphological fingerprint database for gene-morphology coupling. This ensures that tumor region annotation aligns with the clinical diagnostic criteria combining molecular and histopathological aspects of colorectal cancer, improving the clinical adaptability of the annotation results.

The application discloses a double-branch Raio gating graph aggregation multi-modal meningioma segmentation method, belongs to the cross technical field of computer vision and medical image processing, is used for meningioma segmentation, and comprises the following steps: preparing a data set, constructing a deep neural network model, and performing neural network training; inputting the data set into the trained deep neural network model; first inputting a double-branch multi-modal input module to output a multi-modal fusion feature tensor; then inputting the multi-modal fusion feature tensor into a hierarchical Swin Transformer encoder to output three-dimensional segmentation masks of enhanced tumors, tumor cores and whole tumor regions of meningioma. Through double-branch multi-modal input fusion, hierarchical Swin Transformer coding, edge perception modulation and Laplace gating graph aggregation, high-precision three-dimensional segmentation of enhanced tumors, tumor cores and whole tumor regions of meningioma is realized.

The application provides a kind of based on deep learning's absorbed dose prediction method, device, equipment and medium, it is related to medical image processing technical field, comprising: obtaining the treatment of patient's pre-test multi-modal data;According to clinical examination data and the mask map of the target tumor region and normal key organ region of pre-set drug, space coding is carried out, and the biomarker feature map of the patient to be tested is constructed;According to the pre-treatment medical image and biomarker feature map, a preset first deep learning model is used for prediction, to generate the simulated treatment initial medical image of the patient to be tested;According to the simulated treatment initial medical image, a preset second deep learning model is used for prediction, to generate the post-treatment predicted dosimetry parameter map of the patient to be tested.The application can improve the accuracy of nuclide absorbed dose prediction evaluation.

This invention proposes a three-dimensional glioma image segmentation method, electronic device, and storage medium. The method includes: acquiring three-dimensional volume data from multimodal magnetic resonance imaging; fusing the three-dimensional volume data with prior information on orientation boundaries to obtain orientation-boundary-enhanced three-dimensional volume data; decomposing the orientation-boundary-enhanced three-dimensional volume data into a two-dimensional slice sequence and performing inter-slice context modeling to aggregate information from adjacent slices to generate a target feature volume; dynamically inferring discrete token combinations based on the target feature volume using a discrete token vocabulary and an attribute predictor to generate high-level semantic cue features; inputting the target feature volume into an orientation-aware dual-domain enhancement branch to obtain enhanced features; and inputting the enhanced features and high-level semantic cue features together into a sparse hybrid expert decoder to output segmentation probability maps of three nested tumor regions. This invention significantly improves the boundary accuracy and small target recognition capability of glioma subregion segmentation, thereby enhancing the accuracy of image segmentation.

This invention relates to a method for tumor region analysis in pathological images, belonging to the fields of medical image analysis and computational pathology. Addressing the problem of coarse localization caused by existing methods relying solely on block-level appearance features and ignoring kernel structure patterns, this invention explicitly models kernel-level spatial proximity and directional consistency by constructing an orientation-weighted kernel map. Combined with graph neural networks and a multi-instance learning framework, it achieves high-precision tumor region localization using only slice-level labels. The method includes the following steps: spatially consistent whole-slice image decomposition, orientation-prior-based kernel map construction, orientation-constrained graph representation learning, and kernel map-guided multi-instance learning aggregation. This invention can be used for weakly supervised localization and classification of tumor regions in whole-slice images.

This invention provides a suspension containing radioactive microspheres, a method for preparing the same, and its applications. The radioactive microspheres comprise resin microspheres and a radionuclide loaded on the resin microspheres. The resin microspheres are made of at least one of polystyrene, polyethylene, or divinylbenzene, or a cross-linked polymer thereof. The average particle size of the radioactive microspheres is 25 μm to 40 μm. By controlling the non-spherical ratio of the radioactive microspheres to within 5%, the distribution density of the microspheres in the tumor region is increased, thereby enhancing the tumor-killing effect; furthermore, it can reduce the incidence of adverse events.

The application discloses a kind of low-quality MRI-oriented unsupervised cross-domain brain tumor segmentation methods.It includes CDGM-Net, the CDGM-Net includes three collaborative components: initial mask generation module IMG, frequency decomposition matching module FDM and frequency cross fusion module FCM;Specifically comprising the following steps: step S1.MRI image input and feature extraction;Step S2. Generate initial mask, obtain the initial foreground mask of query image feature;Step S3. Frequency decomposition, i.e. the first stage of FDM module;Step S4. Frequency distribution matching, i.e. the second stage of FDM module, obtain the low-frequency feature of aligned query image and aligned query high-frequency image feature;Step S5. Frequency cross fusion, i.e. FCM module: generate final brain tumor segmentation mask.The advantage is: in low-quality MRI scene, noise interference can be more effectively suppressed and the structure consistency of tumor region is maintained, the effectiveness and practical value of frequency domain alignment and cross-frequency fusion strategy in cross-dataset brain tumor segmentation task are verified.

This invention discloses a brain multimodal MRI synthesis system based on segmentation assistance and retrieval enhancement, comprising: a data preprocessing module for acquiring brain multimodal MRI data and performing standardized preprocessing to obtain usable modal images; a training module for training a designed brain multimodal MRI synthesis model based on the usable modal images to obtain a model with optimal performance; and an application module for inputting the brain multimodal MRI data to be processed into the model with optimal performance and outputting the corresponding target modal image. This invention combines segmentation assistance and retrieval enhancement techniques with brain multimodal MRI data to provide high-fidelity synthesis of target modal images and consistency constraints on the anatomical structures of pathological regions, improving the reconstruction fidelity of complex tumor regions and effectively solving the problem of computer-aided analysis.

The application provides a CaVM-Unet-based brain tumor MRI image segmentation method and device for missing modalities, and the method comprises the following steps: S1: acquiring a brain tumor MRI image dataset of four modalities, completing image preprocessing, data enhancement, modality missing marking and dataset division, and constructing a model standard input; S2: constructing a local feature extraction module and an intra-modality visual mamba module IVMB, independently extracting local features and capturing global dependency for each modality; S3: constructing a causally guided multi-modality interaction fusion module CGMIF, and realizing causally constrained multi-modality fusion under the condition of missing modalities; S4: constructing a dual regularization feature module DRFB, and stabilizing feature distribution and focusing on a tumor area in a decoding process; S5: training and optimizing the CaVM-Unet model by using a causally constrained loss and a multi-stage segmentation loss; and S6: segmenting a brain tumor by using the CaVM-Unet model for a multi-modality brain tumor MRI image.

This application discloses a treatment bed and its manufacturing method. The treatment bed includes a bed board and a support structure disposed inside the bed board. The bed board includes a first surface and a second surface arranged opposite to each other. The support structure includes a transition unit and a plurality of arrayed grid units. The grid units extend from the first surface to the second surface of the bed board, and adjacent grid units are connected by the transition unit. The grid units of this application extend along the longitudinal support direction of the bed board. While sacrificing the lateral moment of inertia of the support structure, it increases the longitudinal moment of inertia of the support structure, thereby increasing the longitudinal stiffness of the bed board and its bending stiffness under vertical loads. Therefore, the treatment bed of this application can reduce deformation caused by the patient's weight, ensure that the actual position of the tumor area is consistent with the center position of the medical linear accelerator, improve the positioning accuracy of the treatment bed, and meet the requirements of precise radiotherapy.

A system for automated tumor margin determination in radiological images, comprising: an image acquisition interface configured to receive radiological image data from one or more imaging modalities; a preprocessing unit operationally coupled to the image acquisition interface and configured to perform intensity normalization, spatial resampling, and noise reduction on the received image data to generate standardized image inputs; a feature extraction unit comprising a plurality of convolutional layers arranged in a hierarchical structure and configured to extract spatial features of different orders of magnitude from the standardized image inputs;a transformer coding unit operationally coupled to the feature extraction unit and configured to generate context-sensitive feature representations by applying self-attention operations over spatial areas of the extracted features; a fusion unit operationally coupled to both the feature extraction unit and the transformer coding unit and configured to combine features derived from convolutions and context representations derived from transformers into a unified feature map; a decoding unit operationally coupled to the fusion unit and configured to generate a segmentation map corresponding to the tumor regions by incrementally increasing and reconstructing the spatial resolution; a boundary refinement unit configured to improve the delineation of tumor margins in the segmentation map;a processing unit that is operationally coupled with the preprocessing unit, the feature extraction unit, the transformer coding unit, the fusion unit, the decoding unit, and the boundary refinement unit, wherein the processing unit executes instructions stored in a memory unit to perform automated tumor boundary delineation; and a display interface configured to overlay the delineated tumor boundaries onto the radiological image data.

This invention discloses an automatic segmentation method, system, and storage medium for breast cancer ultrasound images. The method includes: acquiring a breast ultrasound image dataset, performing standardized preprocessing, multi-expert annotation integration, and data augmentation to obtain high-quality data; constructing a basic Attention U-Net model and two improved models, CSWin-Unet and MRCST-Net, respectively, to achieve gradient optimization of segmentation performance through attention gating mechanism, cross-shaped window attention, and residual convolution-Transformer parallel module; using a unified training framework and four indicators—mean intersection-over-union (MIoU), accuracy (Acc), Kappa coefficient, and Dice coefficient—and selecting the optimal model by averaging the results after validation; and performing end-to-end tumor region segmentation on the input breast ultrasound image based on the optimal model. This invention can help improve the efficiency and accuracy of early breast cancer diagnosis.

The application relates to the technical field of image processing, in particular to a bladder cancer contour drawing method and system based on ultrasonic images. The method comprises the following steps: acquiring bladder ultrasonic images and contrast images under each view angle; obtaining a muscle layer infiltration depth factor according to the shape, length and surrounding pixel gray scale of blood vessels in each region in the ultrasonic images, and determining a deviation error factor of each region under each view angle; obtaining a pulling error sensitive factor according to the volume change of the bladder cavity, the distance between the tumor region and the texture region of the urinary muscle bundle, the overall tumor displacement direction and the local muscle bundle main direction in each frame of the bladder ultrasonic images under each view angle, and then obtaining the registration error cumulative value of each region under each view angle; and comprehensively obtaining a drawing error coefficient by combining the deviation error factor, the registration error cumulative value and the pulling error sensitive factor, adjusting an initial edge reservation parameter, and obtaining a bladder cancer contour drawing result. The application improves the accuracy of the bladder cancer contour drawing result.

The present application relates to the technical field of digital image processing, in particular to a tumor interstitial proportion quantitative evaluation system based on pathological images. The present application automatically obtains tumor epithelial region, tumor interstitial region, tumor region and tumor infiltration front region based on image segmentation, without pre-selecting histological evaluation position for visual evaluation by artificial selection, avoiding the subjective experience dependence of traditional methods, and improving the efficiency of tumor interstitial proportion evaluation. In addition, the present application comprehensively quantitatively evaluates the first tumor interstitial proportion under the overall scale of the to-be-tested pathological image, the second tumor interstitial proportion under the scale of the tumor infiltration front region and the third tumor interstitial proportion under the scale of the tumor region, and finally obtains the tumor interstitial proportion score value which can reflect the tumor interstitial proportion characteristics of different anatomical positions, further improving the accuracy of the evaluation result. The preset rule is to select the window tumor interstitial proportion with the highest tumor interstitial proportion value.

This invention relates to the field of image processing technology, and particularly to a method and system for three-dimensional reconstruction of gynecological tumor MRI images based on deep learning. The method includes: processing coarsely labeled tumor regions using a target detection model based on the original image to generate bounding boxes for small lesions; segmenting the images using these bounding boxes to obtain images of small and non-small lesions; proportionally reducing the size of the small lesions to obtain lesion images; selecting samples based on the generated boundary regions, calculating the mean grayscale value and difference coefficient, and weighted merging to obtain normal tissue images; compressing the tumor core region and boundary region to obtain the overall tumor image; stitching the lesion images, normal tissue images, and tumor images together to form a low-resolution image to train the model, and then segmenting and extracting features to construct a three-dimensional tumor model. This invention preserves the features of small lesions and boundaries, improves segmentation accuracy, constructs a high-precision three-dimensional model, and adapts to clinical diagnostic needs.

The present invention relates to the technical field of medical images, and specifically relates to a segmentation method and apparatus for magnetic resonance vascular architectural imaging (VAI). The method and apparatus involve: performing data preprocessing on a VAI image set; constructing a deep learning model, and using the VAI image set, which has been subjected to data preprocessing, to train the deep learning model, so as to optimize a model parameter; and using the trained and optimized deep learning model to perform automatic tumor region segmentation on a new VAI image, so as to generate a segmentation result. In the present invention, by using a deep learning model such as a convolutional neural network (CNN), the features of vascular architecture can be automatically learned, and accurate segmentation is performed with regard to microvascular changes in a tumor region.

A method for treating a tumor in a subject is provided, the method comprising introducing nanoparticles into the tumor, generating alternating RF magnetic fields microwave electromagnetic waves; measuring temperatures of the tumor and of healthy tissue in close proximity to the tumor; and adjusting one or more properties of the RF magnetic fields and / or microwave electromagnetic waves based on the measured temperatures, such that the combination of the RF magnetic fields and the microwave electromagnetic waves cooperate to heat the tumor region to an ablative temperature without substantially heating the healthy tissue which is in close proximity to the tumor.

The application relates to the technical field of digital pathological image analysis, and particularly discloses a hepatocellular carcinoma blood vessel pattern analysis system and application thereof. The hepatocellular carcinoma blood vessel pattern analysis system comprises an image acquisition module, a tumor region detection module, a blood vessel segmentation module, a blood vessel subtype classification module and a blood vessel pattern analysis module. The blood vessel subtype classification module is used for identifying blood vessel subtypes in a blood vessel binary mask image. The blood vessel pattern analysis module is used for identifying a blood vessel pattern, and the hepatocellular carcinoma is classified and prognosis evaluation is performed according to the blood vessel pattern. The system constructed by the application can realize automatic identification of blood vessel structures and subtypes in hepatocellular carcinoma tissues, and can stably and accurately complete hepatocellular carcinoma classification and determination based on the hepatocellular carcinoma blood vessel pattern, thereby solving the technical bottlenecks that the hepatocellular carcinoma blood vessel evaluation in the prior art depends on artificial interpretation, is highly subjective, has poor consistency, and is difficult to realize quantitative analysis of blood vessel morphological characteristics, and lacks precise hepatocellular carcinoma classification and prognosis evaluation based on the blood vessel pattern.

The application discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and specifically comprises the following steps: acquiring patient brain multi-modal image data and extracting three-dimensional tumor regions and corresponding perfusion parameters; constructing a biological target region hierarchical structure containing tumor overall regions and hypoxic subregions; obtaining a relative hypoxia degree parameter by calculating the cerebral blood flow ratio of the hypoxic subregion to other regions of the tumor; deconstructing historical treatment case data to establish a dose-therapeutic effect correlation parameter set; training a dose adjustment strategy model for the hypoxic subregion; and generating a final radiotherapy dose plan scheme according to the dose adjustment value output by the model combined with a clinical guideline basic dose. The application overcomes the limitations of traditional uniform dose irradiation through a data-driven dose decision mechanism, thereby providing an objective and reliable evaluation basis for clinical selection and optimization of personalized treatment schemes.

The application provides a pathological section and MRI image registration method and device based on biomechanical elastic deformation, which comprises the following steps: obtaining a three-dimensional digital twin of a tumor region, obtaining a digital resection region in the three-dimensional digital twin based on a preset surgical margin; performing ex vivo dynamics simulation on the digital resection region to obtain a tissue deformation result; obtaining an optimal deformation section with maximum mutual information in the tissue deformation result and a pathological section; obtaining an inverse transformation deformation field between the optimal deformation section and the three-dimensional digital twin, and projecting a tumor region edge of the pathological section to the three-dimensional digital twin based on the inverse transformation deformation field. The scheme realizes fine local nonlinear adaptation of the section by constructing an ex vivo dynamics finite element simulation technology of a volume shrinkage function and performing local deformation with the pathological section edge information as a constraint, quantitatively restoring the global non-uniform shrinkage of the ex vivo tissue and the differential local deformation of the tumor and normal tissue.

The application provides a tumor closed anti-diffusion ablation device, and relates to the technical field of tumor ablation devices.The device comprises a vertical frame, a vertical groove is formed in the surface of the vertical frame, and a mounting frame is slidably installed in the vertical groove; a treatment assembly is fixed to the outer end of the mounting frame; a pushing assembly is arranged on the top of the treatment assembly; a surrounding assembly is arranged at the bottom of the treatment assembly; and a treatment bed is arranged at the bottom of the surrounding assembly; the treatment assembly comprises a storage layer, and a plurality of groups of ultrasonic lamp groups are equidistantly and slidably inserted into the storage layer; a patient lies on the treatment bed; the position and range of the tumor of the patient are determined in advance through medical diagnosis; then the position and height of the treatment assembly are adjusted so that the surrounding assembly is attached to the skin of the patient; the treatment assembly is pushed out through the pushing assembly; the ultrasonic treatment area is consistent with the tumor area of the patient; the surrounding assembly adjusts the surrounding area according to the ultrasonic treatment area; and thus the tumor of the patient is treated accurately and efficiently.

The application belongs to the technical field of medical image processing and computer-aided diagnosis, and discloses an orbital lymphoma and inflammatory pseudotumor classification method, system, device and terminal, in the obtained orbital DCE-MRI image, the tumor region and the eye cone triangular region in the original image are manually segmented and pretreated; the neural network is used for extracting the features of the tumor region, and the features are subjected to clustering statistical analysis as the tumor region features; the eye cone triangular region is subjected to feature extraction as a similarity judgment standard, and the self-classification and self-recovery network is used for extracting the eye cone region features; the original image is used as the input network and combined with the features of the tumor region and the eye cone triangular region to train the network model; the multi-modal orbital data is input into the classification model for processing, so that the orbital lymphoma and inflammatory pseudotumor are classified. The application comprehensively considers the texture features of the tumor and the depth features of the eye cone region, effectively improves the prediction accuracy, and has the characteristics of high accuracy.

This invention relates to the field of surgical navigation technology for tumor treatment, specifically to an intelligent navigation system for nasal skull base tumor surgery based on multimodal image fusion. The system includes an image acquisition module for acquiring and rigidly registering CT and MRI images to determine the initial tumor region; a tumor invasion degree acquisition module for quantifying the local tumor invasion degree by combining CT grayscale distribution characteristics and MRI apparent diffusion coefficient characteristics; an invasion path acquisition module for simulating a fully covered digital invasion path based on anisotropic diffusion of image features; a risk infiltration degree acquisition module for comprehensively considering distance, direction, and invasive activity to assess the risk of invasion path infiltrating key structures; and a tumor boundary acquisition module for obtaining the tumor biological boundary by correcting the anatomical boundary through risk infiltration degree. This invention effectively improves the accuracy and safety of nasal skull base tumor surgery by acquiring the tumor biological boundary.

The present application relates to a kind of nasopharyngeal carcinoma distant metastasis risk prediction method and system based on cross-modal Transformer and depth consistency loss.Its method includes: collecting the multi-modal MRI image data and structured clinical data of patient;Using pre-training model to obtain tumor region mask, according to which T1, T1C and T2 three modal MRI image is cut, and the image of region of interest containing peritumoral microenvironment is constructed;Image is input three-dimensional image encoder, and deep image feature is extracted, while clinical variable is embedded coding;Through cross attention mechanism, image and clinical feature are fused to model, and the comprehensive feature representation of patient level is obtained;Based on the fusion feature, continuous type distant metastasis risk score is output, and combined with auxiliary classification branch and depth consistency loss, joint optimization is carried out, and the prediction result is obtained.The present application effectively focuses on tumor and peritumoral microenvironment information, enhances the deep layer interaction of multi-modal feature and early high-risk identification ability, and improves the accuracy of nasopharyngeal carcinoma distant metastasis risk prediction.

The present application relates to the technical field of intelligent medical treatment, in particular to a PD-1 immunotherapy liver cancer curative effect prediction method based on machine learning, comprising the following steps: S1. data acquisition and screening; S2. enhanced CT image standardization processing; S3. tumor region of interest (ROI) delineation; S4. radiomics feature extraction; S5. feature screening and stability evaluation; S6. class imbalance processing; S7. feature fusion; S8. curative effect prediction. The present application ensures data consistency through standardized CT image acquisition and preprocessing; precise ROI delineation avoids background interference; multiple types of radiomics feature extraction realizes quantitative characterization of tumor morphology, texture and other microscopic information; combined with clinical feature fusion, the defects of insufficient information dimension of a single data type are made up, so that the comprehensive feature set can fully reflect the tumor biological characteristics, which can more accurately distinguish responding patients from non-responding patients, and the prediction result credibility is greatly improved.

The application provides a non-muscular invasive bladder cancer postoperative recurrence prediction method and system, the method comprising: obtaining preoperative enhanced CT images of a target object, urine SIM2 gene methylation detection results and clinical pathological characteristics; pre-processing the preoperative enhanced CT images and delineating a tumor region of interest, and extracting imageomics features and deep learning features to construct a preliminary multi-modal feature set; performing feature screening and dimension reduction processing on the imageomics features, and fusing the processed imageomics features with the preliminary multi-modal feature set to obtain a multi-modal feature set; inputting the multi-modal feature set into a pre-constructed prediction model to output a non-muscular invasive bladder cancer postoperative recurrence risk probability and risk level of the target object, wherein a first layer of the prediction model is constructed by using a plurality of heterogeneous base learners, and a second layer is constructed by using a meta learner. The application can realize fine stratified management of non-muscular invasive bladder cancer patients.

The application discloses a kind of based on magnetic resonance auxiliary ablation treatment method and device, method includes: detecting the current operation process of tumor ablation operation;When detecting preoperative simulation, scan image is obtained based on three-dimensional magnetic resonance scanning, and organ region and tumor region are segmented, to assist in generating optimal probe strategy;When detecting intraoperative navigation, real-time magnetic resonance scanning is carried out in the process of probe insertion, current magnetic resonance image is obtained, and probe tip, probe angle and tumor coordinates are identified, to assist probe to reach tumor;When detecting intraoperative thermometry, magnetic resonance sequence scanning is carried out in the process of probe ablation, multiple echo magnetic resonance image is obtained, and current temperature distribution and thermal ablation range are displayed in real time until tumor is completely inactivated.

The application discloses a kind of endometrial carcinoma molecular typing methods based on cascaded multiple instance learning, method is by the binary mask of tumor area generated to endometrial carcinoma whole section image preprocessing, to extract the image block in region of interest and construct instance set by dyeing normalization processing;Using PatchVMamba instance encoder, the feature of each image block in instance set is extracted to obtain the instance feature vector of each image block, and the instance feature bag of each whole section image is formed;Using attention mechanism, the instance feature bag is weighted and aggregated to obtain the slice-level feature representation;The slice-level feature is input into the three binary classifiers arranged in cascade to obtain the probability value corresponding to the three subtypes of endometrial carcinoma respectively, and a unified threshold is used for cascade judgment of the subtype classification of endometrial carcinoma to output the final typing result.The cascaded structure of the application makes the consistency of the typing result and the diagnosis of pathological experts high, and realizes accurate typing.