42 results about "Intestinal polyp" patented technology

The invention belongs to the technical field of medical image processing, and discloses an intestinal polyp classification method based on an improved CLIP model. Comprising the steps of collecting a data set and screening and cutting collected images; an SE attention mechanism is added in the Vision Transform block, and polyp image features are learned; the method comprises the following steps: introducing a pathological description text labeled by a professional doctor into Adpater-BERT, and learning pathological character features; and the two features are fused by adopting adaptive weight, and finally classification is carried out. Through the optimization strategy, the improved CLIP model can improve the accuracy of classification of adenomatous polyps and proliferative polyps while keeping relatively low calculation overhead.

The invention relates to the technical field of medical image processing, in particular to an intestinal polyp segmentation method and system based on a spiral scanning state space model, and the method comprises the following steps: S1, obtaining and preprocessing an image; s2, performing multi-scale context feature coding; s3, self-adaptive frequency domain feature enhancement is carried out; s4, performing decoding and segmentation prediction; and S5, carrying out threshold processing to generate a binary segmentation mask image. According to the method, the boundary integrity of an irregular target is kept through a dynamic multi-focus spiral scanning strategy, and the adaptive frequency domain filtering module is introduced to enhance the discriminability of key features, so that the segmentation precision and robustness in complex medical image tasks such as intestinal polyp and the like are comprehensively improved.

The invention discloses an intestinal polyp identification method based on images and multi-modal data. The method comprises the following steps: acquiring pathological images of a plurality of microscopes, objective magnification information corresponding to the pathological images, and text information corresponding to enteroscopes; performing data preprocessing on each frame of acquired pathological image and text information to obtain a training data set; a multi-scale multi-modal feature classification network is constructed, the training data set is used for training, and a trained multi-scale multi-modal feature fusion classification network is obtained; the method comprises the following steps: collecting a microscope pathological image of a patient, obtaining corresponding enteroscope text information, respectively carrying out data preprocessing, and inputting into a multi-scale multi-modal feature fusion classification network to obtain disease category prediction of a model. According to the method, pathological images and enteroscope text information are fully fused, accurate classification and prediction of disease types are finally achieved, the effect and efficiency of multi-modal diagnosis of intestinal diseases are improved, and therefore the accuracy of patient diagnosis is improved.

The invention discloses a lightweight polyp segmentation method based on multi-scale differential features and spatial attention. The method comprises the following steps: preprocessing and dividing an intestinal polyp image data set; then, a lightweight class segmentation model LightRGC-SCNet comprising an encoder-decoder structure is constructed; in the encoding stage of the model, a multi-scale differential feature extraction module is adopted to enhance edges and extract multi-scale features, in the decoding stage, a DySample up-sampling module is guided to dynamically fuse the features and complete up-sampling through joint space attention, and on this basis, a semantic-spatial information fusion module is utilized to collaboratively optimize output features; finally, a combined loss function is adopted to train the model, and performance evaluation and segmentation result output are completed on a test set, a hierarchical processing mechanism and an encoder-decoder architecture are combined, the complexity of the model is remarkably reduced, meanwhile, accurate segmentation of the intestinal polyp is achieved, and the method is suitable for clinical application scenes with limited computing resources.

The invention discloses a colon polyp segmentation method and device based on a CT image and a storage medium. The method comprises the following steps: carrying out initial segmentation on an intestinal CT image to obtain an initial segmentation result containing a colon wall; performing geometric analysis on the initial segmentation result to extract target geometric features related to the colon wall; marking a target colon wall seed voxel in the initial segmentation result based on the target geometric feature; growing according to the target colon wall seed voxels to obtain all suspected polyp blocks; and calculating the center coordinates of the suspected polyp blocks and sorting the center coordinates to segment the colon polyp. By means of the scheme, the polyp segmentation efficiency and segmentation precision can be improved.

Provided herein are pharmaceutical compositions, therapeutic combinations, devices and methods for treating, ameliorating, reversing, causing the remission of, and / or preventing (acting as a prophylaxis, or preventing the initiation of) an inflammatory bowel disorder (IBD) or inflammatory bowel disease (IBD), Ulcerative Colitis; Crohn's disease; J-pouch; fistulising Crohn's disease; a Colitis which can be microscopic, lymphocytic or collagenous; an eosinophilic colitis; indeterminate colitis; idiopathic colitis; diverticulosis and diverticulitis; relapsing diverticulitis; constipation associated inflammatory bowel disease and / or small intestinal bacterial overgrowth; Irritable Bowel Syndrome (IBS) with or without diarrhoea, constipation or pain predominant IBS; periodontitis; rheumatoid arthritis; respiratory infections, appendicitis, vascular disorders such as thrombophlebitis; bacteremia; osteomyelitis; septic shock; Alzheimer's disease; Lemierre syndrome (postanginal sepsis); colonic polyps or adenomas (optionally hyperplastic, adenomatous or serrated adenomas) or preventing the growth of colonic polyps or adenomas, bowl cancer, or metastases (optionally preventing the initiation or promotion of bowl cancer or metastasis); pharyngitis; otitis; sinusitis; and any disease, symptom or condition caused or exacerbated by a Fusobacteria (optionally, a F. nucleatum or F. varium) infection. In alternative embodiments, pharmaceutical compositions comprise rifaximin alone or in combination with other antibiotics or drugs.

A use of a flavonoid compound and a drug for preventing and / or treating familial intestinal polyposis. In the present invention, it is found for the first time by experiments that the compound as shown in formula I can correct the disorder of β-catenin accumulation in cell nuclei caused by APC gene mutation to promote the transfer of β-catenin to cell membranes, can promote the restoration of the normal morphology of intestinal villi with dysplasia and deformity, and can decrease the number of intestinal polyps caused by APC mutation, thereby achieving the effect of preventing and treating familial intestinal polyposis.

The utility model relates to the technical field of teaching models, in particular to an intestinal tract model which comprises a first intestinal tract model, a second intestinal tract model and a third intestinal tract model, in the intestinal tract model provided by the invention, the second intestinal tract model is transparent, and a plurality of polyp models are arranged on the second intestinal tract model; one end of the second intestinal model is detachably connected with the first intestinal model, and one end, far away from the first intestinal model, of the second intestinal model is detachably connected with the third intestinal model, so that the problems that a polyp model cannot be added into the intestinal model due to the fact that a traditional intestinal model generally adopts an integrated structure, and a polyp model cannot be added into the intestinal model are solved; therefore, the problem that the practicer cannot practice the excision operation of the intestinal polyp is solved.

The application discloses a lightweight polyp segmentation method based on multi-scale differential features and spatial attention, comprising the following steps: firstly, preprocessing and dividing an intestinal polyp image dataset; then, constructing a lightweight segmentation model LightRGC-SCNet containing an encoder-decoder structure; in the encoding stage of the model, a multi-scale differential feature extraction module is adopted to enhance edges and extract multi-scale features; in the decoding stage, a joint spatial attention guiding DySample up-sampling module is used to dynamically fuse features and complete up-sampling, and on this basis, a semantic-spatial information fusion module is used to cooperatively optimize output features; finally, a combined loss function is used to train the model, and performance evaluation and segmentation result output are completed on a test set; the application combines a hierarchical processing mechanism and an encoder-decoder architecture, significantly reduces the complexity of the model, realizes accurate segmentation of intestinal polyps, and is suitable for clinical application scenarios with limited computing resources.

This invention discloses a polyp segmentation system based on multi-scale feature fusion and edge awareness, belonging to the field of polyp segmentation technology. The system first extracts features from intestinal polyp images at multiple scales, from deep to shallow, forming feature maps with at least four levels of resolution, laying the foundation for subsequent multi-level representation. Then, multi-scale feature refinement is performed on the higher-resolution feature maps, introducing a multi-receptive-field structure to enhance the expression of local structure and texture details, while retaining the lowest-resolution features as the starting point for fusion. Furthermore, cross-scale feature fusion is performed in ascending order of resolution, achieving progressive supplementation of semantic features at different scales. Finally, edge aggregation processing using unified channels, unified sizes, and fixed Gaussian convolution kernels strengthens the consistency of structural boundary region representation. The overall scheme has the advantages of clear structural hierarchy, full feature utilization, and strong ability to express polyp edge details.

The application discloses a multi-scale intestinal polyp segmentation method fusing an attention mechanism. The basic features of the method are as follows: 1. a multi-scale effective semantic fusion module is constructed to extract more abundant and effective multi-scale semantic information; 2. a new encoding-decoding deep network segmentation model is constructed to improve polyp segmentation accuracy; the method extracts sufficient context information and global information under different receptive fields, and filters out as many features useless for the segmentation task as possible, overcomes the defects that semantic information is limited and a large amount of redundancy exists in the traditional encoding-decoding structure, and has excellent segmentation and generalization performance for two-dimensional enteroscopy images with polyp regions of different shapes and different sizes.

The invention relates to the technical field of data recognition, and particularly discloses a colon polyp recognition and segmentation method and system based on a digestive endoscope. The method comprises the steps of image preprocessing, candidate region extraction, feature extraction, candidate region classification and colon polyp segmentation. According to the method, the candidate areas in the colonoscope image are classified by using the support vector machine classifier, so that the detection accuracy and efficiency of the colon polyp are improved. Meanwhile, a morphological analysis method is adopted, more detailed colon polyp information including accurate contour, area, perimeter and the like can be obtained, and the accuracy of recognition and segmentation can be improved.

The invention relates to the related technical field of anorectal medical treatment, in particular to an anorectal minimally invasive rotary cutting endoscopic instrument which comprises a displayer, and an installation limiting mechanism is arranged in a rotary telescopic mechanism. According to the anorectum minimally invasive rotary cutting endoscopic instrument, when the positions of the rotary cutting knife and the probe are adjusted, the motor is started to drive the threaded rod to rotate, so that the telescopic block and the telescopic rod connected with the telescopic block stretch out and draw back stably in the telescopic groove, and the probe stretches out and draws back along with the telescopic block to probe anorectum tissue of different depths; a micro illuminating lamp helps a doctor to observe through a display screen and accurately adjust and control according to an operation key, internal hemorrhoids, rectal polyps and the like can be cut, a clamping groove of a rotating rod and a clamping block of the rotary cutter are matched to guarantee stability, looseness and displacement are prevented, operation errors are reduced, when the rotary cutter is installed, the clamping block is inserted into the clamping groove, a limiting block moves in a limiting groove, a telescopic spring pushes a limiting rod into a limiting hole to be locked, and when the rotary cutter is abraded, the rotary cutter is replaced. The rotary cutter is pulled to enable the fixing block to overcome resistance to retreat from the limiting hole, convenience and high efficiency are achieved, instrument maintenance is facilitated, and operation requirements are met.

A joint detection kit for detecting intestinal polyp factors, and a preparation method therefor, a detection method therefor and the use thereof. The joint detection kit at least comprises a plurality of test strips for detecting the following indicators: M2-pyruvate kinase, matrix metalloproteinase 9, myeloperoxidase, glutathione S-transferase Pi, cytidine deaminase, retinol binding protein 4, serine protease inhibitor F2, calprotectin and fecal occult blood. Conjugate pads (2) of each reagent strip are coated with detection antibody-colloidal gold conjugates. Detection lines (3) of the reagent strip are coated with specific capture antibodies, and the specific capture antibodies on each test strip are different. The joint detection performed by the joint detection kit can effectively improve the sensitivity and specificity of the detection on intestinal polyps. The detection time is merely 15 min, with the detection rate of more than 91%. Therefore, the accuracy on the detection and diagnosis of colorectal cancer caused by intestinal polyps is improved.

The invention discloses a small sample intestinal polyp image semantic segmentation method, which comprises the following steps: feature extraction: mapping intestinal polyp images input by a support branch and a query branch to a depth feature space by using a group of backbone networks sharing weight; carrying out multi-modal feature alignment; performing general semantic alignment among the branches; prototype generation: capturing a prototype set of classes on the separated target foreground supporting features, query decoupling features and branch supporting multi-mode alignment features, calculating the similarity between query mixed features and each prototype on the prototype set position by position, and quickly positioning and segmenting a polyp region in a query picture according to the maximum similarity. According to the method, through decoupling of the mixed features, mutual interference among different semantic features in the mixed features is reduced, and the perceptual ability of the segmentation model to polyps and surrounding tissue structures is enhanced; meanwhile, the robustness, the reliability and the generalization of the semantic segmentation model of the intestinal polyp image can be further improved.

The present invention discloses a method and device for intestinal polyp segmentation based on deep fusion of endoscopic images. The method includes: collecting endoscopic intestinal polyp images and performing preprocessing to obtain a training set and a test set; constructing a deep feature fusion module by using a gated axial attention mechanism module and a sliding window attention mechanism module; constructing an intestinal polyp image segmentation model by using the gated axial attention mechanism module, the deep feature fusion module, and an attention gating module; training the intestinal polyp image segmentation model with the training set to obtain an optimal intestinal polyp image segmentation model; inputting the real-time collected endoscopic intestinal polyp image into the optimal intestinal polyp image segmentation model to obtain a predicted segmentation image. The advantages of the present invention are: solving the problem of lack of a large amount of labeled data, and having lower training cost and training difficulty.

The invention discloses an intestinal polyp detection method based on improved RTDETR-r18. The method comprises the following steps: carrying out labeling and image preprocessing on a public combined data set; adopting an improved PCRConv-Block as a basic module of a backbone network to perform feature extraction on the feature map; cCA-HSFPN is adopted for feature fusion, spatial dependency modeling is enhanced through a coordinate attention mechanism, and effective fusion of features of different scales is achieved; feature coding is carried out, position coding information is added, and feature enhancement is carried out through a Transform encoder; a DRBRC3 module is adopted to replace a Repc3 to construct better global feature representation and multi-scale fusion; performing prediction through a Transform decoder, and outputting bounding box coordinates and category probabilities corresponding to each intestinal polyp; according to the method, the feature extraction capability of the model under a complex background is enhanced, the model is lightweight, and the detection capability of the model on detail features is improved; therefore, the model can process image features on different scales, the parameter quantity and the calculation complexity of the network are reduced, and the detection precision is improved.

The present application belongs to the technical field of deep learning in computer vision, and provides an intestinal polyp image segmentation method for simulating polyp growth. The present application decouples the complete ground truth map into a Gaussian ground truth map and a main body ground truth map to pay more attention to position information and main body information respectively; three feature extraction branches are constructed to extract features, and are supervised by the Gaussian ground truth map, the main body ground truth map and the complete ground truth map respectively; and a dynamic attention guidance module is designed to more efficiently aggregate features from different branches. The present application realizes a more accurate polyp segmentation model, which can accurately segment polyps of various complex shapes and backgrounds.

The present invention discloses a method for detecting intestinal polyps based on deep supervision and step-by-step learning. The method uses PVT_V2 to extract multi-scale features from the images captured by colonoscopy; the extracted multi-scale features are input into a detail enhancement module to output the features after detail enhancement; the features after detail enhancement are input into a guidance fusion module to fuse the low-level features with the high-level features under the guidance of the high-level features; the features of the highest layer are input into a multi-branch decoder module to obtain the features after multi-scale information fusion decoding; the features after detail enhancement and the output result of the previous multi-scale decoder are input into a multi-scale decoder to gradually learn the output features of each layer of the multi-scale decoder; the output features of the multi-scale decoder pass through a one-layer convolutional layer to obtain the detection result. The present invention continues to highlight the multi-scale detail information in the multi-scale decoder, realizes the precise positioning and accurate detection of intestinal polyps, and has good generalization ability.

Disclosed are methods and compositions for the treatment or prevention of intestinal polyps or prevention of cancer in a patient who has been identified as being at risk for developing intestinal polyps or intestinal cancer. The disclosed methods and compositions include rapamycin, a rapamycin analog, or another such inhibitor of the target of rapamycin (TOR).

The invention discloses an application of engelhardia roxburghiana leaf general flavone extract in preparation of a medicine for preventing / treating ulcerative colitis. Experiments prove that the engelhardia roxburghiana leaf total flavone extract can effectively improve pathological changes of intestinal tissues of ulcerative colitis model mice. According to the present invention, the engelhardia roxburghiana leaf total flavone extract provides significant inhibition and improvement effects for AOM / DSS induced DAI score, colon length, intestinal polyp quantity and pathological histological change, and can recover the expression of barrier proteins ZO-1 and Occludin by significantly reducing the levels of inflammatory factors IL-1beta, IL-6, TNF-alpha and IL-17A in intestinal tissues; inflammatory factor induced epithelial barrier function damage and permeability increase are relieved, and then the intestinal epithelial barrier function is maintained; the balance of intestinal flora is recovered, and the local microenvironment of the intestinal tract is improved, so that the effect of treating ulcerative colitis is achieved. The engelhardia roxburghiana leaf general flavone extract has an application prospect of treating ulcerative colitis diseases.

The invention relates to the technical field of medical instruments, in particular to endoscopic surgery integrated equipment for excising intestinal polyp, which comprises a handle, a main cannula, a camera and an electrotome, and an auxiliary cannula is fixed with the front end of the main cannula; the auxiliary assembly comprises a cylindrical part in butt joint with the front end of the auxiliary sleeve, a suction cup is slidably sleeved with a radial hole in the bottom of the front end of the cylindrical part, the front end of the collecting cavity is connected to the bottom of the rear wall of the radial hole, an arc-shaped hole channel is formed between the front end of the piston cavity and the top end of the radial hole, and a bendable air pipe with the front end fixedly in butt joint with a top hole of the suction cup is slidably sleeved with the arc-shaped hole channel. The piston shaft comprises a plug body slidably sleeved with the piston cavity and a pipe fitting with the middle fixedly sleeved at the center of the plug body and the front end fixedly butted with the rear end of the bendable air pipe; the pressure exhaust assembly is used for adjusting air pressure on the front and rear sides of the plug body through air pipes I and II; the negative pressure assembly is used for providing negative pressure for the pipe fitting and the collecting cavity through the third air pipe and the fourth air pipe. Therefore, endoscopic surgery equipment can safely and efficiently cut off intestinal polyps.

The invention discloses a colon polyp composite processing method and processing system, and the method can carry out the classification prediction of an output image based on an image classification model, and extracts a class label with the highest prediction probability and a confidence score of the class label. A label and a confidence score are superposed on an original frame, polyps with different properties are converted into different colors, polyp time points are marked to form a colored time progress bar, and the progress bar and an original colonoscope video are combined together to form a brand new video as an output result. A quick review tool is provided for subsequent colonoscope minimally invasive surgery; the device can help an endoscopic physician to accurately master the position and form of the polyp and quickly position the polyp found previously, so that the operation time is shortened, the risk of postoperative complications is reduced, and the diagnosis and treatment efficiency is improved.

Disclosed are methods and compositions for the treatment or prevention of intestinal polyps or prevention of cancer in a patient who has been identified as being at risk for developing intestinal polyps or intestinal cancer. The disclosed methods and compositions include rapamycin, a rapamycin analog, or another such inhibitor of the target of rapamycin (TOR).

The invention provides a primer combination and a kit for detecting intestinal polyp and / or colorectal cancer and application of the primer combination and the kit. The related product for detecting the intestinal polyp or the colorectal cancer comprises a primer combination, a reagent and a kit, based on the RNA level of at least one of EPCAM, CCND1, S100A4 and SHNG7 genes in an excrement sample, noninvasive, accurate and simple detection of the intestinal polyp or the colorectal cancer is achieved with extremely high sensitivity and specificity, and especially detection of progression-stage adenoma (gt; 1 cm adenomatous intestinal polyp) and / or early colorectal cancer.

The application discloses a use of a ganoderma lucidum spore and grifola frondosa composition in preparation of a preparation for assisting in relieving colorectal cancer. The composition comprises ganoderma lucidum spore oil, squalene, grifola frondosa extract, selenium-rich yeast and the like, and is prepared into soft capsules by using a cold preparation method. A mouse model experiment induced by AOM / DSS proves that the soft capsules can significantly relieve weight loss of mice, reduce a disease activity index, improve colon shortening, inhibit expression of inflammatory factors TNF-alpha and IL-6 in serum and colon tissues, reduce intestinal inflammatory reactions, repair colon crypt structure and intestinal mucosal damage, down-regulate expression of a proliferation marker Ki 67, up-regulate expression of an apoptosis marker Caspase-3, induce tumor cell apoptosis, regulate intestinal flora structure, restore relative abundance of Bacteroidetes and Firmicutes, and reverse abnormal increase of Verrucomicrobia. The application can act on the whole process from intestinal polyps, precancerous lesions to colorectal cancer development, is safe and stable, can be taken for a long time, and has high application value.

The present invention relates to an intestinal polyp boundary segmentation method based on sparse connection and global feature enhancement. The method uses a sparse harmonic connection network as the encoder backbone to extract features from an input intestinal polyp image. A dilated spatial convolutional pyramid pooling module is used to parallel sample the deepest features at different sampling rates. The results of the parallel sampling are input into a partial feature decoder together with the features of the last two layers of the encoder for decoding to generate a rough initial prediction map. A shallow attention module is used to extract shallow boundary information from the rough initial prediction map and shallow features for preliminary refinement of the boundaries of the initial prediction map. A global feature enhancement module is used to process deep features for guiding subsequent boundary segmentation of the initial prediction map. A reverse attention module is used to obtain complete boundaries from multi-scale features to complete intestinal polyp boundary segmentation. Based on the traditional encoding and decoding structure, the present invention greatly enhances the boundary modeling capability and the regional hole filling capability.

The invention discloses a UNet-based colon polyp image segmentation system and method, and the method comprises the steps: constructing and training a colon polyp segmentation model, inputting a to-be-segmented colorectal polyp image into the trained colon polyp segmentation model, and obtaining a polyp segmentation image; the colon polyp segmentation model takes UNet as a main network, and comprises a cascade convolution module, a convolution attention module, a feature fusion module and a boundary refining module; according to the model provided by the invention, the segmentation accuracy of the colon polyp edge can be effectively improved, and meanwhile, the robustness of the network is improved.

The invention belongs to the technical field of traditional Chinese medicine, and particularly relates to a traditional Chinese medicine composition for preventing and treating the recurrence and canceration of colorectal polyps, and a preparation method and application thereof. The raw materials of the traditional Chinese medicine composition include atractylodes, coix seeds, oldenlandia diffusa, licorice, Pseudostellaria heterophylla, Curcuma zedoariae, rhodiola rosea, bergamot, Patrinia patriniae, Amomum villosum, Gleditsia sinensis, Euryale ferox, Coptis chinensis, Paeonia lactiflora, Herba euphorbiae, and Fritillaria thunbergii. The traditional Chinese medicine composition has the effects of clearing turbidity and detoxifying, dispersing knots and eliminating symptoms, can effectively prevent the recurrence after intestinal polypectomy, delay the course of intestinal adenoma, delay the process of canceration, reduce the recurrence rate after intestinal polyp surgery and reduce the incidence of colorectal cancer, has clear efficacy and low price.

To identify a component that effectively contributes to the treatment (suppression) of colorectal cancer, the risk of developing which increases with aging.SOLUTION: In the present invention, whether coumestrol effectively contributes to the treatment (suppression) of colorectal cancer is investigated using ApcMin / + mice, which are a model of familial intestinal tumor disease. The ApcMin / + mice showed an age-related increase in the number of intestinal polyps and developed cachexia. In contrast, in mice to which coumestrol is administered, the age-related increase in the number of intestinal polyps and the onset of cachexia were found to be significantly suppressed. From these findings, it can be concluded that coumestrol effectively contributes as a component for treating (suppressing) the onset of colorectal cancer, and is useful as an agent for treating (suppressing) colorectal cancer.SELECTED DRAWING: Figure 1