43 results about "Kidney tumor" patented technology

The invention discloses a CT contrast image kidney tumor segmentation method based on a three-dimensional convolution neural network. The method firstly roughly segments the kidney region in the CT contrast image, the kidney and tumor are labeled separately, and the data set is generated. Then the training set is put into the convolution neural network based on pyramid cisternization and step-by-step feature enhancement module, and the training model is obtained. The new kidney data is predicted by the training model, and the segmentation mask of kidney tumor is obtained. The invention also provides a CT contrast image kidney tumor segmentation system based on a three-dimensional convolution neural network. The invention mainly solves the problem of difficult image segmentation of kidney tumor, and the invention can directly obtain a segmentation mask of kidney tumor.

Genes that are differentially expressed in subtypes of renal cell carcinomas are disclosed as are their polypeptide products. This information is utilized to produce nucleic acid and antibody probes and sets of such probes that are specific for these genes and their products. Methods employing these probes, including hybridization and immunological methods, are used to determine the subtype of a renal cell tumor sample from a subject based on the differential expression of such genes that is characteristic of the cancer subtype.

The invention provides a point interaction deep learning segmentation algorithm specially for solving the kidney tumor segmentation problem in a medical image. The algorithm is composed of a point interaction preprocessing module, a bidirectional ConvRNN unit and a core deep segmentation network. The algorithm starts from a tumor center position provided by an expert; in 16 directions with uniformintervals, 16 image blocks with the size of 32 * 32 are intensively collected from inside to outside according to the step length of 4 pixels to form an image block sequence, a deep segmentation network with sequence learning is used for learning the inside and outside change trend of a target, the edge of the target is determined, and segmentation of the kidney tumor is achieved. The method canovercome the influences of low contrast, variable target positions and fuzzy target edges of medical images, and is suitable for organ segmentation and tumor segmentation tasks. Compared with the prior art, the method has the following characteristics: 1) the interaction mode is simple and convenient; (2) a Sequence Patch Learning concept is provided, and a sequence image block is used for capturing a long-range semantic relationship, so that a relatively large receptive field can be obtained even in a relatively shallow network; and 3) a brand-new ConvRNN unit is provided, the inside and outside change trend of the target is learned, the interpretability is relatively high, the actual working mode of doctors is met, and the final model is high in precision and strong in applicability.

The invention discloses an improved U-net kidney tumor segmentation method. The method comprises the following steps: combining a kidney and a kidney tumor together through a pixel superposition method; extracting the image for a plurality of times by using convolution at the encoder part, and generating corresponding segmented kidney and kidney tumor images by using transposed convolution at thedecoder part; introducing a residual learning unit into the encoder; and carrying out optimization training by using a Luoshi loss function in network training. According to the method, a deep residual network and a The Lovasz-Softmax loss function are combined to achieve the semantic segmentation method based on the ResUnet, the characteristics of the image are better extracted by utilizing the Lovasz hinge residual network, the improved method is more excellent than an original method, and the segmentation precision of the kidney tumor is effectively improved.

This invention is in the field of chemical restructuring of pharmaceutical agents known to cause tissue toxicity as a side effect, by producing their orotate derivatives. More particularly, it concerns orotate derivatives of the anthracyclines, doxorubicin and daunorubicin, that are found to reduce levels of the pharmaceutical agent in noncancerous tissues. There orotate derivatives are equally efficacious in inhibiting the SCCAKI-1 kidney tumor in animals and the reduction in the heart tissue of doxorubicin compared with doxorubicin HCl suggests a reduction in toxicity induced by free radical generation by the anthrracyclines.

This invention provides an anti-cancer immunogenic agent(s) (e.g. vaccines) that elicit an immune response specifically directed against renal cell cancers expressing a G250 antigenic marker. Preferred immunogenic agents comprise a chimeric molecule comprising a kidney cancer specific antigen (G250) attached to a granulocyte-macrophage colony stimulating factor (GM-CSF). The agents are useful in a wide variety of treatment modalities including, but not limited to protein vaccination, DNA vaccination, and adoptive immunotherapy.

The kidney benign and malignant tumor classification method based on multi-view information cooperation comprises the following steps: step 1) medical image preprocessing: performing data enhancementprocessing on images of three views of kidney CT; 2) constructing a multi-view convolution network sub-model for the image of each view; and 3) constructing a multi-view information collaborative convolutional neural network model, unifying the outputs of the sub-models of the three views to the same neuron classification layer, and finally inputting a Sigmoid function to obtain a classification result. A penalty function is added for false positive cases, and greater penalty is given to reduce the occurrence of false positive conditions; and 4) kidney tumor benign and malignant classification: inputting a kidney CT image to be detected into the multi-view information collaborative convolutional neural network model constructed in the step 3), and performing network output to obtain a benign and malignant result of the tumor. According to the method, the multi-view image information of different kidney tumors is combined and fully utilized, the accuracy of kidney tumor benign and malignant classification can be improved, and meanwhile, the problem of poor model generalization ability caused by insufficient neural network training data due to lack of case image data is avoided.

The invention discloses an image segmentation method for a kidney tumor, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and dividing the abdomen scanning imageinto a data set and a training set; S2, performing down-sampling preprocessing on the acquired abdomen scanning image to obtain a scaled image; S3, determining an area of interest of the preprocessedimage in the step S2 by using global position information of the abdominal space, performing image segmentation, and performing training and prediction by a U-shaped kidney tumor segmentation network;S4, outwards expanding the abdomen scanning image in the step S1 for a certain range, segmenting images of the left kidney and the right kidney, interpolating all segmented images, and unifying the interpolated images into the same data distribution to obtain left and right kidney VOI images; S5, performing tumor segmentation prediction on the left and right kidney VOI images by a U-shaped kidneytumor segmentation network. Interference of other organs and tissues is effectively avoided, the accuracy of kidney tumor identification and image segmentation is improved, and efficiency is higher.

The invention discloses a kidney tumor segmentation method based on multi-scale feature learning, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and randomly dividing the obtained abdomen scanning image into a training set; S2, preprocessing the abdomen scanning images in the training set to obtain preprocessed images; S3, constructing a multi-scale featurenetwork, and fully capturing global structure information of the image through the network in combination with a pyramid pooling module and a feature pyramid fusion module so as to perform accurate kidney segmentation; S4, predicting and segmenting the image preprocessed in the step S2 through a multi-scale feature network; The invention can be used for effectively detecting kidney tumors with different volumes, false negative results are avoided, and high-accuracy detection results are obtained.

The invention discloses a remote-controllable transparent human abdomen model for explaining disease conditions, which comprises a shell, a model of main abdominal organs, a battery, an air pump and a remote controller. The outer shell is transparent, and there are batteries, an air pump and the main organs in the abdominal cavity inside. The battery powers the air pump and the robotic arm inside the kidney. The kidneys consist of left and right kidneys, each of which has a retractable mechanical arm. The mechanical arm is stretched and moved freely under the command of the remote control, and there is an air bag at the other end, which represents the tumor. The air pump injects gas into the air bag through the plastic tube connected to the air bag to make the air bag bigger and smaller to represent the volume change of the tumor. The airbag moves with the robotic arm and can reach any position in the kidney to display tumors in various parts of the kidney. When explaining the disease, the doctor can intuitively describe the normal shape and adjacent structure of the abdominal organs, mainly the kidney, simulate the location and size of the tumor in the kidney, and explain the operation process in detail, so that the patient and his family can fully understand the disease. And it can replace the traditional single organ model for popular science teaching.

The invention discloses a kidney tumor segmentation method based on mixed dimension convolution, and the method comprises the following steps: S1, obtaining an abdomen scanning image, and dividing theobtained abdomen scanning image into a data set and a training set; S2, preprocessing the abdomen scanning image in the data set to obtain a preprocessed image; S3, constructing a mixed-dimension convolution network, and optimizing the feature learning of the mixed-dimension convolution network for the kidney tumor through the network in cooperation with a mixed-dimension convolution module; S4,inputting the preprocessed image into a mixed-dimension convolutional network for prediction, and finally obtaining a segmentation result; 2D, 2.5 D and 3D convolution features of the kidney tumor arelearned at the same time through the hybrid convolution network, and the generalization ability of model features is enhanced through feature fusion of the 2D, 2.5 D and 3D convolution features.

The invention relates to the technical field of image recognition, in particular to a kidney tumor enhanced CT image automatic recognition system based on deep learning and a training method thereof. The system includes an infrastructure unit, a deep learning unit, an image recognition unit and an auxiliary expansion unit. The infrastructure unit is used for providing infrastructure equipment and devices for supporting system operation; the deep learning unit is used for building a learning model, supporting the operation of the image recognition system through deep learning and improving the recognition precision; the image recognition unit is used for inputting a CT examination image of a patient and recognizing the CT examination image to judge the symptom type; and the auxiliary expansion unit is used for improving the functionality of the system by adding various expansion auxiliary applications. The designed system can noninvasively predict the pathological type of the kidney tumor in advance, the diagnosis and treatment efficiency is improved, meanwhile, the workload of doctors is relieved, and in addition, the system can be widely applied to primary hospitals; the training method can improve the recognition precision of the system and improve the efficiency and accuracy of kidney tumor diagnosis.

The present invention relates to a composition for modulating tumor cell dissemination, in particular metastatic cancer cells. In particular, the invention relates to an agent for modulating metastatic tumor cell dissemination for use in the treatment and/or prevention of a metastatic cancer wherein the agent is a capture agent and/or a chemoattractant for tumor cells. The invention also relates to a product, comprising an agent for modulating metastatic kidneys tumor cell dissemination, and to a method of treatment or prevention of cancer.

The invention relates to a U-Net kidney tumor image segmentation method and device based on aggregation interlayer information, and a storage medium. The method comprises the following steps: 1) acquiring an image; 2) image preprocessing; 3) constructing a label, and constructing a corresponding label for the preprocessed image obtained in the step 2); 4) constructing a U-Net model for aggregating interlayer information; 5) training a U-Net model for aggregating interlayer information; 6) segmenting the kidney tumor image, and inputting a kidney tumor image to be segmented into the trained U-Net model of the aggregation interlayer information to realize image segmentation. According to the method, the information in the slice layers and the information between the slice layers are fully utilized to learn context information, and memory consumption and hardware requirements of a computer are reduced. The output extraction features after convolution are realized on multiple scales, so the information redundancy can be reduced, and the segmentation accuracy can be improved.

The invention provides a method for predicting difficulty and complications of a multi-mode kidney tumor kidney protection operation, and belongs to the technical field of medicine. Three imageology comprehensive scoring capabilities of an R.E.N.A.L scoring system, an MAP scoring system and a CSA scoring system are obtained through computer artificial intelligence deep learning; and weighted calculation is performed on basic disease information of the patient, whether anticoagulant drugs, antibiotics and hormones are used or not, preoperative heart, lung and kidney function data and imaging comprehensive scoring ability through computer artificial intelligence deep learning; then, an imaging scoring part of an existing scheme is reserved and fused, more and more comprehensive imaging features are considered compared with all different scoring systems of the existing scheme, the defects of labor consumption, time consumption, instability and the like of manual calculation are overcome, and the result is more accurate and stable.

The invention provides external use traditional Chinese medicine for curing kidney cancer. The external use traditional Chinese medicine comprises a basic remedy and an adjuvant. The basic remedy is formed by 41 crude drugs such as semen strychni, monkshood, radix aconiti agrestis and osteon myospalacem baileyi. The adjuvant is formed by safflower carthamus, liquorice, centipede and Shanxi mature vinegar. The external use traditional Chinese medicine for curing kidney cancer has the obvious function for inhibiting kidney tumors, and has the advantages of being safe, effective, fast, easy to operate, free of pains and economical.

A method of diagnosing the likelihood of recurrence of clear cell renal cell carcinoma is provided. The method involves a) detecting the gene expression signatures of mitochondrial electron transport chain subunits, mitochondrial ribosomal proteins, major histocompatibility complex class II (MHC-II) proteins or combinations thereof in a kidney tumor tissue sample; and b) determining that the subject has an elevated risk of recurrence of clear cell renal cell carcinoma if the gene expression signatures include certain sequences. In another embodiment, the method uses copper levels to diagnose the likelihood of recurrence of clear cell renal cell carcinoma.

The present invention provides a method for classification of kidney tumors through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA expression framework allows optimization of treatment, and determination of specific therapy.

The invention relates to an index evaluation method for renal tumor partial resection risk and postoperative early prognosis. The method comprises the steps: dividing different operation modes into open operations and non-open operations; according to preoperative CT imaging data of a patient, obtaining the sum of a basic size index of a kidney tumor and a position index of the tumor as a preoperative index evaluation index, and establishing an operation difficulty and early prognosis index for preoperative evaluation of the kidney tumor according to different operation modes. Compared with other scoring systems, the system can be more suitable for diversified operation modes at present, and reference suggestions are provided for clinicians. Compared with other scoring systems, the evaluation system for evaluating the operation risk and postoperative early prognosis of renal tumor partial resection based on different operation modes can be more suitable for the diversified operation modes at present; the system is an evaluation system established through prospective research on the basis of the corresponding illness state of a patient, and compared with a previous scoring system (based on retrospective research), the operation risk and postoperative early prognosis can be better evaluated.

The invention discloses a full-automatic segmentation method for kidney tumors. The full-automatic segmentation method comprises an experimental environment part and an algorithm research part. The experimental platform of the invention is a Windows 10 operating system, and the display card of the experimental platform is NVIDIA GeForce GTX 1080Ti. The algorithm research mainly comprises coarse segmentation of a CT image kidney and fine segmentation of a kidney tumor. The operation process of the whole system is as follows: 1, installing a Windows 10 operating system, and configuring an Anaconda version to be Anaconda 3, a python version to be 3.6, a CUDA version to be 10.0, a PyTorch version to be 1.6 and other required installation packages; 2, carrying out preprocessing operation on the data set, wherein the operation is operated in a pycharm which is provided with an Anaconda environment; 3, segmenting the processed data set by using the RAUNet. 4, carrying out post-processing on the pictures output after training. and 5, evaluating the model on the test set through an evaluation index. The main purpose of the invention is to promote the development of intelligent medical treatment, save labor cost, improve segmentation precision, and prevent diseases in advance, thereby ensuring human health.

The invention is applicable to the technical field of computer medical auxiliary diagnosis and treatment, and provides a simulation method for assisting kidney surgery approach. The simulation methodcomprises the following steps of acquiring three-dimensional models of kidney, tumor and skin corresponding to a patient; according to an intersection curve of the kidney model and the tumor model, calculating the longest diameter of the intersection curve and a short axis perpendicular to the longest diameter; establishing a tangent plane through the longest diameter and the short axis, and obtaining a sewing plane which is perpendicular to the tangent plane and passes through the longest diameter; and on the conical surfaces of a pair of reference cones with the short axis as the central axis, selecting a pair of surgical approaches to intersect with the skin model to generate two approach reference points. The invention further provides a simulation system for assisting the kidney surgery approach. Therefore, in the kidney tumor resection operation, the suturing surface and the optimal operation approach point can be automatically simulated; the influence of bones and other abdominal organs is avoided in the operation; and the surgical quality is improved.