37 results about "Colon polyps" patented technology

The invention relates to a colon polyp segmentation method based on a multi-frequency attention guiding network, and the method comprises the steps: inputting a polyp image into a backbone network through constructing the multi-frequency attention guiding network, and extracting the feature information of different layers; a multi-frequency attention guiding module is constructed to obtain high-frequency edge details and a low-frequency global structure by means of partial features, and boundary features are optimized by combining a feature enhancement space attention module. Further, an iteration content guiding attention mechanism is introduced, and attention distribution is dynamically adjusted according to different areas, especially for lesion areas with fuzzy boundaries and complex forms. According to the method, the limitation of the polyp when the boundary is fuzzy and the form is complex is overcome, a multi-stage loss and output aggregation strategy is adopted in training, and high-precision recognition and segmentation of the polyp area are achieved.

The invention provides a hybrid CNN-Transform colon polyp image segmentation method combining edge guidance and a double attention mechanism, and is applied to the technical field of medical data processing. According to the method, a CNN encoder is adopted to extract multi-scale local features, a Transform encoder is adopted to extract global context, an edge probability graph is generated by introducing an edge guide branch based on shallow layer features, a gating coefficient is calculated according to the edge probability graph and / or statistics obtained by intermediate prediction, weighted fusion is performed on the local and global features, and the local feature and the global feature are integrated. And the decoder performs up-sampling step by step and outputs a segmentation result. Compared with the prior art, the method has the advantages in boundary integrity and small target detection. Experiments show that on a Kvasair-SEG data set, the optimal Dice of a verification set of the scheme is 0.892, the optimal HD95 of the verification set of the scheme is 12.2, the Dice of a test set of the scheme is 0.89, and the optimal HD95 of the test set of the scheme of the scheme is 12.3.

The invention provides a colon polyp detection method based on improved YOLOv11, a colon polyp detection model based on a YOLOv11 network is constructed, the colon polyp detection model comprises a backbone network, a neck network and a head network which are connected in sequence, and a UIB module is introduced into the backbone network for realizing extraction of spatial features and reducing model parameter quantity and calculation complexity; the neck network comprises an up-sampling part and a feature fusion part, and a DySample module is introduced into the up-sampling part and is used for dynamically adjusting a sampling weight and a sampling position according to the complexity of input features; an LDConv module is introduced into the feature fusion part and is used for dynamically adjusting the sampling position of a convolution kernel according to the morphological change of the polyp in the peristalsis process to capture the fine features of the deformed polyp. According to the method, the accuracy and efficiency of colorectal polyp detection and classification are improved, meanwhile, the floating point calculation amount and parameter counting are greatly reduced, and the method is of great significance to clinical diagnosis.

The application provides a colonoscopy polyp segmentation method based on a multi-scale space reverse attention mechanism, first, a public colon polyp segmentation dataset is collected on a network, and data enhancement processing is performed, and a training set, a test set and a verification set are divided; then, the colon polyp data is input into a designed pyramid Swin Transformer encoder to extract features at four different stages, and multi-scale feature information is extracted through a multi-scale channel attention module to obtain a preliminary aggregated pre-segmentation result; finally, the pre-segmentation result is input into a space reverse attention mechanism module to gradually supplement the edge structure and detail information of the polyp region, so that the purpose of automatically segmenting the colon polyp is achieved. The application is convenient to use, accurate and efficient, has good colonoscopy polyp segmentation effect, and has excellent generalization performance.

The present invention provides a parallel diagnosis method for colon polyp medical images based on enhanced UNet, specifically comprising: 1. preprocessing of a colon polyp image segmentation dataset; 2. establishing an enhanced UNet model, initializing and training it; and 3. system control and colon polyp medical image detection output. The present invention establishes primary standards and a refined classification set for the classification of colon polyp medical image datasets, providing useful exploration for achieving accurate detection and early prevention of colon polyp medical images. An enhancement module is designed using morphological classification of colon polyps to increase the accuracy of colon polyp medical image detection. Users can set parameter values ​​for the screening range, facilitating independent screening by hospitals and physical examination centers, thereby increasing the flexibility of the system. In addition, the present invention adds parallel execution processes to automatically and continuously screen a large number of colon polyp medical images, accurately detecting specific parts and reducing the workload of doctors.

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

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.

The invention relates to the crossing field of medical artificial intelligence and computer vision, and discloses a colonoscope polyp image generation method based on a bidirectional information constraint diffusion model, and the method comprises the steps: constructing and training a noise prediction network based on conditional mask constraint and dynamic reweighting loss; the colon polyp mirror image generating module is used for generating a colon polyp mirror image of a corresponding polyp tissue with the quality meeting the requirement under the guidance of the condition mask; executing a sampling process based on bidirectional information constraint, dynamically combining a foreground generation image and a real background constraint image, inputting a condition mask to the noise prediction network, and gradually synthesizing a colon polyp mirror image containing a real enteric cavity structure and a condition polyp tissue; and executing a sampling process based on bidirectional information constraint, dynamically combining a foreground generation image and a real background constraint image, inputting a condition mask to the noise prediction network, and gradually synthesizing a colon polyp mirror image containing a real enteric cavity structure and a condition polyp tissue. The performance of the segmentation model can be effectively improved.

The present invention relates to the field of image processing technology, and particularly to a method and system for evaluating the size of colon polyps based on multi-frame matching. A video stream to be evaluated of a colonoscope is obtained, and a colon polyp detection model is used to determine whether there are polyps in the video frames to be evaluated therein. When there are polyps, the position information of the polyps is obtained and the image similarity between two adjacent frames of N subsequent video frames of the video frame to be evaluated is calculated. When the image similarity meets a preset condition, a depth estimation model is used to generate a corresponding depth map. According to the position information of the polyps and the polyp depth information in the depth map, the size of the polyps in the subsequent video frames is calculated. The sizes of the polyps in each subsequent video frame are weighted and calculated, and the standard polyp size of the video stream to be evaluated of the colonoscope is output. The present invention can better predict the depth information of images in a real colonoscope, eliminate the influence of polyp blurring, smooth the display of polyp size output, measure the size of polyps more accurately, and help endoscopic doctors make better decisions.

The invention discloses a colon polyp segmentation system based on multi-scale feature fusion, which comprises an original image acquisition module, a preprocessing module, an SwinFF-Net network module and a result output module, and is characterized in that the SwinFF-Net network module comprises an encoder, a jump connection module and a decoder; the system enhances the capture of detail information in the image by fusing the features of different scales, especially for the conditions that the boundary is fuzzy and the diseased region is difficult to distinguish. The multi-scale feature fusion can help the model to extract effective features from different levels of image information, so that the distinction degree between the colon polyp and the background is improved, and more accurate and more complete polyp segmentation is realized.

The application belongs to the field of intelligent medical computer-aided diagnosis application, and relates to a colon polyp segmentation method based on deep learning fusion and collaborative learning enhancement. The method comprises a feature extraction model, a fusion module and multi-view collaborative learning. The feature extraction model is divided into two branches. One branch uses DeiT-Small to extract global feature information and establish the correlation between each pixel. The other branch uses HardNet-MSEG to extract local feature information and obtain more low-level detail information. In order to improve the segmentation accuracy of small target colon polyp images, based on the public colon polyp image dataset and the initial deep learning single-branch segmentation method, a deep learning technology fusion method is proposed, and multi-view collaborative learning is used to enhance colon polyp segmentation. Compared with the feature extracted by a single deep learning method before improvement, the feature is richer, and the information omission defect of a single branch is compensated.

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.

This invention belongs to the technical field of medical auxiliary equipment, and in particular, relates to a method and apparatus for detecting colon polyps based on endoscopic images. The method comprises the following steps: S1, multi-scale feature extraction based on dilated convolution and a ResNet50 residual network; S2, feature enhancement using frame-assisted contrast learning; S3, prediction based on a CenterNet network; and S4, self-guided bounding box matching during the testing phase. By deploying this efficient detection method on a colonoscopy detection device, the present invention enables real-time and accurate localization of colonoscopic polyps during colonoscopy procedures, improving the efficiency of colonoscopic polyp examination and treatment, reducing the risks associated with multiple examinations and delayed treatment for patients, and providing a strong guarantee for the early diagnosis and treatment of intestinal diseases.

This invention belongs to the field of probiotic screening and application technology, specifically relating to a strain of *Lactobacillus paracasei* and its application in the prevention and treatment of colonic polyps and colon cancer. The *Lactobacillus paracasei*, named *Lactobacillus paracasei* V85, was deposited on January 15, 2026, at the China Center for Type Culture Collection (CCTCC), Wuhan University, China, with accession number CCTCC NO: M2026124. The *Lactobacillus paracasei* V85 provided by this invention is sensitive to common antibiotics and has good biosafety; it possesses strong antioxidant function and a strong inhibitory effect on hemolytic streptococci; it can produce a strong agglutination effect with hemolytic streptococci, which is beneficial for inhibiting the colonization of hemolytic streptococci in the intestine, reducing the risk of colonic polyps and colon cancer caused by hemolytic streptococci, and can promote the proliferation of *Akkermansia myxophilus*. It can be used to prevent and treat colonic polyps and colon cancer caused by hemolytic streptococci, and has broad application prospects.

The invention relates to a segmentation method and system for a non-significant colon polyp in a medical image, and the method comprises the steps: carrying out the collection of an endoscopic image, and obtaining a colon polyp image; performing multi-level feature extraction on the colon polyp image through a multi-branch feature fusion network, and outputting multi-scale features; adding local features in the multi-scale features by adopting a feature fusion strategy, and splicing the local features with the global features to obtain fused features; boundary refining processing is carried out on the fusion features to obtain refined features; and decoding the refined features and outputting a segmentation result graph. Complementation of local features and global features is realized through a multi-branch feature fusion network, and the feature expression ability of the non-significant colon polyp is enhanced; the expressive power and the fusion effect of the multi-scale features are improved through a feature fusion strategy; the RCF module effectively alleviates the segmentation deficiency of the traditional method under the condition of fuzzy boundary, and improves the fine-grained performance of the segmentation result.

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 present invention discloses a method and system for detecting the size of colon polyps under a colonoscope. The present invention combines the YOLOv5 colon polyp detection model and the DepthAnything depth estimation model, which can not only accurately detect polyps in colonoscopy images, but also estimate a precise depth map. Subsequently, the distance between the intestinal wall and the lens during colonoscopy can be determined using the polyp bounding box and the depth map, and the three-dimensional point cloud map of the colonoscopy image in the real world can be reconstructed, the size of the polyp in the point cloud can be calculated, and converted into the actual physical size of the polyp, which is displayed in real time during the operation. The present invention realizes the precise positioning and size measurement of polyps, and avoids the measurement errors caused by the lack of reference objects in traditional methods.

The invention relates to the technical field of traditional Chinese medicine compounds, in particular to a compound traditional Chinese medicine preparation for preventing and treating postoperative recurrence of colonic polyp and a preparation device thereof, and the compound traditional Chinese medicine preparation comprises the following components: baked ginger, fried garden burnet, smoked plum, pomegranate rind, hairyvein agrimony, astragalus membranaceus, fried rhizoma atractylodis, coix seed, coptis chinensis, patrinia, sargentgloryvine stem, cortex moutan, elecampane, radix paeoniae alba and liquorice. The medicinal materials are compatible according to the principle of monarch, minister, assistant and guide, the compound preparation has the effects of relieving diarrhea with astringents, dispersing blood stasis, stopping bleeding, clearing heat and drying dampness, clinical experiments show that the traditional Chinese medicine compound preparation can effectively prevent postoperative recurrence of colonic polyp and improve the living quality of patients, and animal experiments show that the traditional Chinese medicine compound preparation has no toxic or side effect. The traditional Chinese medicine compound preparation can effectively improve the survival rate of colon'inflammation-cancer 'transformation model mice, reduce the number of tumors (malignant colon polyps), reduce tumor load and improve pathological changes.

The invention discloses a colon polyp real-time segmentation method and system fusing multi-scale high-frequency information, and relates to the field of image processing, and the method comprises the steps: obtaining a colon polyp image in real time; inputting the colon polyp image into a trained segmentation model to obtain a segmentation result of the colon polyp image; the high-frequency boundary mining module is used for obtaining high-frequency boundary guide information by adopting the edge gradient amplitude and the dynamic modulation mechanism of the colon polyp image; and the high-frequency boundary guide fusion module group is used for fusing the plurality of scale feature maps obtained by the feature extraction module, and enhancing the boundary features of the colon polyp in the plurality of scale feature maps according to the high-frequency boundary guide information. According to the method, the segmentation precision of the small target colon polyp is improved, and meanwhile, the identification probability of false positive (false report) and false negative (missing report) is effectively reduced.

The present invention discloses a colon polyp segmentation method, device and storage medium, including: inputting colonoscopy image pictures into a feature encoder to obtain multi-level encoded features; for the encoded features, obtaining enhanced features at each level through a context enrichment module; at the same time, the present invention designs a boundary extractor to generate a boundary attention map through multi-level encoded features; the enhanced features are dynamically fused according to the principle from high level to low level through a series of boundary-guided bidirectional attention residual modules, and the boundary is enhanced at the same time to obtain the final colon polyp segmentation prediction result. The present invention constructs a colon polyp segmentation model based on a boundary-guided bidirectional attention residual network, and uses the boundary-guided bidirectional attention residual module and the context enrichment module to obtain accurate segmentation results in many challenging scenarios.

The present invention discloses a method for identifying false positives in polyp detection based on a cross-level incomplete connection mechanism, belonging to the technical field of colonoscopy polyp detection. It includes constructing a spatial multi-path mechanism to locate the characteristic regions of polyps shown on the feature map. The feature fusion main body fuses lesion features of different sizes using feature maps of three scales to form sets of positive sample data and negative sample data. After training the positive samples of the same batch and the same scale, the cosine similarity loss is calculated. The present invention locates the characteristic regions of polyps on the feature map through the spatial multi-path mechanism, forms sets of positive sample data and negative sample data, calculates the cosine similarity loss after training the positive samples of the same batch, and at the same time combines a single-stage model to perform multi-scale sampling on the negative sample data, and combines the maximum likelihood estimation to select the negative sample with the highest confidence score, improving the problem of high false alarm rates in detecting colon polyps using existing R-CNN methods and active learning-based methods.

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.

The invention relates to the technical field of edge segmentation, in particular to an intelligent colon polyp edge sketching method based on medical image.The method comprises the steps that in the colonoscopy process, edge detection is conducted on each frame of image in collected colon internal detection image data, and a plurality of nested intestinal wall contour curves are obtained; determining the normal deviation degree of each position point based on the normal direction change of each position point on the intestinal wall profile curve, and further determining a polyp position point on the intestinal wall profile curve; and based on curvature change and normal direction change of adjacent position points of the polyp position points on the intestinal wall contour curve, determining a polyp edge end point of the polyp position points, and performing colon inner wall polyp contour sketching in each frame of image. The polyp edge recognition precision is effectively improved by accurately positioning the polyp protruding area on the intestinal wall and drawing the edge contour.

This invention belongs to the field of functional probiotic screening and application technology, specifically involving a novel strain of *Lactobacillus plantarum* (…). Lactiplantibacillus plantarum The *Lactobacillus plantarum* strain was deposited on January 15, 2026, at the China Center for Type Culture Collection (CCTCC), Wuhan University, Wuhan, China, with accession number CCTCC NO: M202026133. This strain exhibits strong antioxidant capacity, significant inhibitory and flocculating effects against hemolytic streptococci, and can significantly reduce the levels of pro-inflammatory cytokines IL-6 and IL-1β induced by hemolytic streptococci while increasing the level of the anti-inflammatory cytokine IL-22. It has no toxic effects on the body, good safety profile, and can be used to prevent colonic polyps and colon cancer caused by hemolytic streptococci, showing broad application prospects.

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.

This invention belongs to the field of functional probiotic screening and application technology, specifically relating to a strain of *Lactobacillus plantarum* with the function of preventing and treating colon polyps and colon cancer, and its application. The *Lactobacillus plantarum* was deposited on January 15, 2026, at the China Center for Type Culture Collection (CCTCC), Wuhan University, China, with accession number CCTCC NO: M202026126. This strain can significantly inhibit the growth of hemolytic streptococci that induce colon polyps and colon cancer risk, and produces a flocculation effect with hemolytic streptococci, thus having important application value for the prevention and treatment of colon polyps and colon cancer.

This invention belongs to the field of probiotic screening and application technology, specifically relating to a strain of *Lactobacillus rhamnosus* and its application in the prevention and treatment of colon polyps and colon cancer. The *Lactobacillus rhamnosus* is named *Lactobacillus rhamnosus* (…). Lacticaseibacillus rhamnosus S28, deposited on January 15, 2026, at the China Center for Type Culture Collection (CCTCC), Wuhan University, Wuhan, China, with accession number CCTCC NO: M2026129, exhibits strong inhibitory activity against *Streptococcus bovis* and demonstrates a strong agglutination effect with it. It can be used to prevent or treat colonic polyps and colon cancer caused by *Streptococcus bovis*. This invention also provides probiotic pellets containing *Lactobacillus rhamnosus* S28, which exhibit strong resistance, effectively protecting the probiotics from gastric digestion, ensuring their smooth arrival in the intestines, and achieving rapid disintegration within the intestines, showing broad market prospects.

The application belongs to the field of intelligent medical computer-aided diagnosis and relates to a colon polyp segmentation method with universality. The method is based on a coding and decoding network structure, the encoder adopts a Mix Transformer structure with multi-scale output and efficient self-attention mechanism, and the decoder is a novel structure designed according to the multi-scale output of the encoder. The simple and effective convolutional neural network is used for the low-layer output of the encoder, then the multi-scale feature fusion module is used for the lower-layer, middle-layer and high-layer outputs of the encoder, then the results of the convolutional neural network and the multi-scale fusion are respectively input into the CBAM attention mechanism and the SE Block, and finally the results are input into the similarity aggregation module together, so as to enhance the feature representation. Compared with the existing deep learning method, the performance of the application is better on multiple data sets, and the application has good universality and effectiveness.

The invention discloses a colon polyp detection method and system based on improved ResNet, and the method comprises the steps: constructing and training a colon polyp detection network, inputting a to-be-detected colorectal polyp image into the trained colon polyp detection network, and obtaining a polyp segmentation map; the colonic polyp detection network takes ResNet as a main network, adopts a PvTv2 encoder to process an input image, and comprises a local feature enhancement module, a multi-scale feature fusion module and a reverse attention module. According to the model provided by the invention, the segmentation accuracy of the polyp edge can be effectively improved, the local features and the global features are mutually supplemented, and the generalization of the network is enhanced.

The present application relates to the technical field of edge segmentation, and particularly relates to a colon polyp edge intelligent sketching method based on medical images, which comprises the following steps: in the process of colonoscopy, edge detection is performed on each frame of image in the collected colon internal detection image data to obtain a plurality of nested intestinal wall contour curves; based on the normal direction change of each position point on the intestinal wall contour curve, the normal deviation degree of each position point is determined, and then the polyp position point on the intestinal wall contour curve is determined; based on the curvature change and the normal direction change of the adjacent position points of the polyp position point on the intestinal wall contour curve, the polyp edge end point of the polyp position point is determined, and the colon inner wall polyp contour sketching is performed in each frame of image. The present application accurately locates the polyp protruding area on the intestinal wall and performs edge contour sketching, thereby effectively improving the polyp edge recognition accuracy.