18 results about "Intestinal polyp" patented technology

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 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 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.

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 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.

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 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 present application relates to the technical field of medical image processing, in particular to a kind of intestinal polyp segmentation method and system based on helical scanning state space model, comprising the following steps: S1, obtain and pre-process image;S2, multi-scale context feature coding;S3, adaptive frequency domain feature enhancement;S4, decoding and segmentation prediction;S5, thresholding processing generates binary segmentation mask image.The present application keeps the boundary integrity of irregular target through dynamic multi-focus helical scanning strategy, and introduces adaptive frequency domain filtering module to enhance the discriminability of key features, so as to comprehensively improve the segmentation accuracy and robustness in complex medical image tasks such as intestinal polyp.

This invention belongs to the interdisciplinary field of medical devices and artificial intelligence, and discloses a method and system for measuring the size of intestinal polyps based on monocular depth vision. The method includes: preprocessing endoscopic keyframe images; sequentially generating a depth map using a monocular depth estimation model; locating the polyp using a target detection model; and generating a pixel-level segmentation mask using an instance segmentation model. Next, the polyp body and tail are separated based on the distance distribution analysis from the contour points to the centroid, and the pixel diameter is obtained by geometric fitting of the body contour. Then, edge depth values ​​are sampled in the target's internal region using a contour inward contraction method based on distance transformation. Finally, the edge depth and pixel diameter are used as features input to a pre-trained machine learning regression model to predict the millimeter / pixel ratio and calculate the physical size of the polyp. This invention achieves fully automated and high-precision measurement of polyp size, effectively solving the problems of high subjectivity and low accuracy in existing technologies.

The invention relates to the technical field of image processing, in particular to an intestinal polyp recognition method, system and product based on recognition model assistance, and the method comprises the following steps: S1, obtaining an image of the inner wall of an intestinal tract; s2, identifying and marking a target polyp range on the image through a polyp detection model; s3, inputting the image into a depth map prediction model to obtain a depth map of the target polyp; s4, obtaining edge information and real size information of the target polyp based on the target polyp range and the depth map of the target polyp; and S5, inputting the target polyp image information in the plurality of preset directions into the polyp form prediction model, outputting the form type of the polyp form, and calculating and outputting the volume of the polyp according to the form type of the polyp form. According to the application, accurate recognition, form classification and volume estimation of the polyp can be realized based on an artificial intelligence neural network computer-aided detection system through the intestinal endoscope image.

The invention relates to the field of biological diagnosis, in particular to detection and application of a novel biomarker 5 '-tiRNA-Tyr for colorectal cancer diagnosis. The 5 '-tiRNA-Tyr which is remarkably and highly expressed in tissues and plasma of a colorectal cancer patient is found, and the nucleotide sequence of the 5'-tiRNA-Tyr is shown as DEQ ID NO.1. The expression level of the blood plasma 5 '-tiRNA-Tyr can be used for distinguishing colorectal cancer patients from healthy physical examinees and intestinal polyp patients, and good diagnosis efficiency is achieved. The detection primer and the kit based on the 5'-tiRNA-Tyr provide a new effective means for diagnosis and early screening of the colorectal cancer.

This invention relates to a method and system for identifying intestinal polyps, belonging to the field of image recognition technology. The method includes: acquiring endoscopic images of intestinal polyps and their corresponding pathological slide images; performing shallow feature extraction on the endoscopic images to obtain a shallow feature map, and then performing depth feature extraction on the shallow feature map to obtain a macroscopic feature vector of the intestinal polyp; cropping the pathological slide image into several non-overlapping image blocks, extracting features from each image block to obtain a feature vector of the image block, and calculating the attention weight of the feature vector of each image block; calculating a microscopic feature vector of the intestinal polyp based on the feature vector of each image block and its corresponding attention weight; fusing the macroscopic feature vector and the microscopic feature vector to obtain a fused feature vector; and predicting the category of the intestinal polyp based on the fused feature vector. This invention can effectively identify intestinal polyps.

The invention relates to an intestinal polyp recognition method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining an endoscope image related to an intestinal polyp and a pathological section image corresponding to the endoscope image; performing shallow feature extraction on the endoscope image to obtain a shallow feature map, and performing deep feature extraction on the shallow feature map to obtain a macroscopic feature vector about intestinal polyp; cutting the pathological section image into a plurality of non-overlapping image blocks, performing feature extraction on each image block to obtain a feature vector of the image block, and calculating an attention weight of the feature vector of each image block, on the basis of the feature vector of each image block and the attention weight corresponding to the feature vector, a microscopic feature vector about the intestinal polyp is obtained through calculation; fusing the macroscopic feature vector and the microscopic feature vector to obtain a fused feature vector; and predicting the fusion feature vector to obtain the category of the intestinal polyp. According to the invention, the intestinal polyp can be effectively identified.