14 results about "Kidney tumor" patented technology

Device and methods for detection of kidney cancer using urine samples are disclosed herein. The device and methods enable isolation and quantification of kidney tumor cells in urine samples from a subject in need thereof.

The application discloses a kidney tumor image segmentation method and device based on a multi-scale dynamic segmentation kernel and a storable medium, relates to the technical field of image processing, and comprises the following steps: acquiring a kidney tumor image dataset, and dividing the dataset into a training set and a test set; constructing a multi-scale dynamic segmentation network model, training the multi-scale dynamic segmentation network model by using the training set, and testing the multi-scale dynamic segmentation network model by using the test set after training; and outputting the trained and tested multi-scale dynamic segmentation network model, which is used for subsequent kidney tumor image segmentation. The application solves the problems of effective feature enhancement of a kidney tumor image, positioning of a kidney tumor in a highly similar segmentation background, and the definition of a kidney tumor segmentation boundary.

This application relates to a method, apparatus, device, and storage medium for classifying ultrasound videos of renal tumors. The method includes acquiring multimodal ultrasound videos of renal tumors and annotating multiple multimodal ultrasound videos to obtain a dataset; processing the data in the dataset to generate unified ultrasound image data; extracting features from the ultrasound image data to obtain multi-scale features; performing modal fusion processing on the multi-scale features based on an attention mechanism to obtain fused features; predicting the location and category of the renal tumor in any frame of the multimodal ultrasound video based on the fused features to obtain renal tumor prediction results on multiple single frames; filtering target features from the renal tumor prediction results based on confidence levels and performing prediction processing based on the target features to obtain target classification results. This application is beneficial for improving the classification accuracy of ultrasound videos of renal tumors.

The invention relates to the technical field of surgical planning, in particular to a kidney tumor accurate resection path planning method and system based on three-dimensional modeling, and the method comprises the following steps: obtaining a kidney tissue tumor surface area, extracting a line segment towards a renal gate direction, extending a direction vector, constructing a passing guide line, and continuing a path to a compression direction; extracting a blood vessel crossing path section, dividing an interference area, adjusting the direction of a turning section, connecting a tumor and a renal gate, and generating a resection path planning result set. According to the method, by constructing a direction line segment set extending along a structure compression trend, advancing a path space trend segment by segment, combining a replacement mechanism of regions with inconsistent directions, depending on density division of crossing edge point segments, defining the distribution position of a traffic limited region, adjusting repeated turning segments through direction lines, and eliminating path fluctuation and turn-back interference, the traffic limited region is determined. The extending direction penetrates through the tumor center and is connected with the starting point path, the path track integrally keeps smooth extension, the space layout is more ordered, and the matching performance of the passing line segment structure is enhanced.

The application discloses a kidney tumor image segmentation method and system based on boundary extraction, relates to the technical field of kidney tumor image segmentation, and comprises the following steps: a PVTv2 main stem extraction network is used to extract main stem features in an endoscope image, and low-level features including texture, color and the like are generated; a feature self-attention module is used to increase edge detail information, and the transmission of encoder-decoder information is performed; an efficient hybrid decoding module is used to perform decoding while ensuring local continuity, more high-level semantic features are obtained, and an accurate prediction mask is output. The application can effectively extract edge detail information, solve the segmentation difficulty caused by the different appearances of kidney tumors due to the movement of a laparoscope, and improve the accuracy of segmentation.

The invention relates to a novel liposome polydopamine nanoparticle and application thereof in treating renal cell carcinoma. The preparation method of the nanoparticles comprises the following steps: adding liposome into si-circPVT1 dissolved in ribonuclease-free water, and carrying out vortex mixing to form a si-circPVT1 liposome suspension; dispersing the dopamine PDA modified liposome into a Tris-HCl solution, then adding dopamine hydrochloride, and stirring to obtain dopamine PDA modified liposome; then dispersing in a streptavidin solution, and fully oscillating at 4 DEG C under a dark condition to obtain a compound; and mixing the complex with a biotin-labeled MUC12 antibody solution for incubation, and washing the obtained suspension with a PBS (Phosphate Buffer Solution) to obtain the novel liposome polydopamine nanoparticles marked as SCLPM-NPs. The product prepared by the invention realizes targeted delivery of kidney cell cancer cell lines, and the SCLPM-NPs can effectively target tumor tissue parts and inhibit the growth of kidney tumors.

The application discloses a marker group for kidney cancer screening and diagnosis and application thereof, and belongs to the field of molecular biological technology.The marker group comprises at least four polypeptides shown in sequences of SEQ ID NO.1-38.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of kidney cancer.The auxiliary diagnosis result is better than that of one of main current clinical diagnosis schemes for kidney tumors, i.e., imaging examination, and enhanced CT (sensitivity and specificity are 78.67% and 87.88%, respectively).The early screening result is better than that of a high-frequency color Doppler ultrasound instrument (diagnostic sensitivity is 94.87%, and specificity is 80%) applied to the routine examination of the abdomen in a health examination; and multi-gene methylation combined analysis (sensitivity is 62.9%, and specificity is 87.0%).

The invention relates to a kidney tumor image segmentation method and system based on multi-scale feature extraction, and the method comprises the steps: extracting the multi-scale features of a kidney tumor image to be processed, dividing feature similarity groups, carrying out the optimization through combining the texture and shape, and obtaining similar feature groups; generating a complementary information flow, adaptively enhancing an interaction weight between groups by using an attention mechanism, and obtaining an enhanced complementary feature representation; evaluating and denoising noise, establishing association mapping among groups based on features after denoising, and performing multi-scale feature fusion and boundary refining by adopting a U-Net network to obtain tumor boundary representation; and the boundary fuzzy degree is evaluated, iterative optimization is carried out by adopting a graph convolutional network, optimized collaborative hierarchical representation is obtained, and finally a high-precision kidney tumor segmentation result and a pixel-level contour are output. According to the method, through multi-scale feature cooperation, attention interaction and boundary optimization, the problems of boundary blur and noise sensitivity in kidney tumor segmentation are effectively solved, and the accuracy and robustness of segmentation are remarkably improved.

The application discloses a kidney tumor segmentation method based on multi-scale feature extraction, adopts a mainstream encoder-decoder architecture, proposes a feature refinement module in the encoder path, divides and fuses the features of each stage on the channel, so that more fine-grained local features can be extracted on the encoder path, which is very helpful for the target with large size change such as kidney tumor; meanwhile, a multi-scale feature extraction module is proposed in the decoder path, the receptive field can be expanded without introducing too much memory consumption by nesting a small U-shaped network in the path, and the global features can be extracted more effectively, in addition, the multi-scale feature extraction module also includes a soft threshold denoising module, the soft threshold denoising module can automatically select a threshold to eliminate the noise-related information, so that more discriminative features can be extracted.

The application belongs to the technical field of medical information processing, and relates to a kidney tumor postoperative recurrence risk prediction system and method based on multi-modal fusion, which comprises the following steps: acquiring multi-modal data of a patient at multiple time points after operation, generating fusion correlation features reflecting the cooperative relationship between different modal data, obtaining kidney-specific physiological parameters quantifying the physiological and pathological state of the kidney, and a recurrence risk index; constructing the fusion correlation features and the recurrence risk index into a fusion feature time sequence and a recurrence risk index time sequence changing with time, and performing cooperative analysis on the fusion feature time sequence and the recurrence risk index time sequence to generate a recurrence probability value of the kidney tumor after operation; the application solves the problems that the fine characterization of the physiological and pathological dynamic evolution process of the kidney, a specific organ, after operation is lacked, the pertinence of the model is not strong, and the timeliness and accuracy of the prediction are limited.

The invention belongs to the technical field of medical information processing, and relates to a kidney tumor postoperative recurrence risk prediction system and method based on multi-modal fusion, and the method comprises the steps: obtaining the multi-modal data of a patient at a plurality of time points after the operation, generating a fusion correlation feature reflecting the cooperative relationship between different modal data, and carrying out the prediction of the recurrence risk of the kidney tumor after the operation; kidney specific physiological parameters and recurrence risk indexes for quantifying kidney physiological and pathological states are obtained; constructing the fusion correlation features and the recurrence risk indexes into a fusion feature time sequence and a recurrence risk index time sequence which change along with time, and performing collaborative analysis on the fusion feature time sequence and the recurrence risk index time sequence to generate a postoperative recurrence probability value of the kidney tumor; according to the method, the problems that the pertinence of the model is not strong and the timeliness and accuracy of prediction are limited due to the lack of fine description of the specific organ, namely the kidney, in the postoperative physiological and pathological dynamic evolution process are solved.

The application discloses a boundary auxiliary kidney tumor segmentation method based on a Transformer, and is applied to the technical field of kidney tumor segmentation and comprises the following steps: a PVTv2 backbone extraction network is used to extract backbone features in an endoscope image, and an adaptive context extraction module is used to adaptively extract the backbone features in different stages; a mixed feature capture module is used to mix the features obtained after adaptive extraction in different stages with output features of a previous-stage mixed feature capture module, and the features are decoded through an SA-FF module; a boundary auxiliary module is used to extract boundary contour features in the backbone features, and the boundary contour features are fused with features finally output by the mixed feature capture module to obtain a boundary auxiliary kidney tumor segmentation prediction graph. The application can effectively extract local detail information, and solve the feature misplacement problem during fusion of high-level semantics and low-level semantics and the boundary blur problem of the kidney tumor.

The invention discloses an image processing method and device for high-precision recognition of kidney tumors. The method comprises the following steps: acquiring to-be-processed kidney tumor image information; preprocessing the to-be-processed kidney swelling image information to obtain initial image information; performing identification processing on the initial image information by using an image identification model to obtain target identification result information; the image recognition model comprises M composite modules and one composite fusion module. Therefore, the method is beneficial to weakening the interference effect of the meaningless information on the kidney tumor image recognition, thereby improving the analysis precision of the kidney tumor image and the image recognition efficiency.

The application relates to a deep learning-based full-automatic segmentation and benign-malignant classification method for kidney small tumors, which comprises the following steps: S1, dividing a 3D kidney image into left and right part images and flipping; S2, a segmentation model is used for segmenting the original image and the flipped image to obtain a predicted segmentation image; the predicted segmentation images are weightedly averaged to obtain a fused tumor predicted segmentation image, the fused tumor predicted segmentation images are compared, a fused tumor predicted segmentation image with a larger tumor prediction area is identified as containing a tumor, and the tumor segmentation result is placed in the original image and the flipped image corresponding to the side to form a segmentation image to be classified; S3, the segmentation image to be classified is input into a classification model to predict the tumor class in each segmentation image to be classified, and the final benign-malignant classification result is obtained by averaging and fusing the tumor classes in the segmentation images to be classified. The application can realize efficient and accurate segmentation and classification of kidney tumors.