8 results about "Tumor margin" patented technology

A system for automated tumor margin determination in radiological images, comprising: an image acquisition interface configured to receive radiological image data from one or more imaging modalities; a preprocessing unit operationally coupled to the image acquisition interface and configured to perform intensity normalization, spatial resampling, and noise reduction on the received image data to generate standardized image inputs; a feature extraction unit comprising a plurality of convolutional layers arranged in a hierarchical structure and configured to extract spatial features of different orders of magnitude from the standardized image inputs;a transformer coding unit operationally coupled to the feature extraction unit and configured to generate context-sensitive feature representations by applying self-attention operations over spatial areas of the extracted features; a fusion unit operationally coupled to both the feature extraction unit and the transformer coding unit and configured to combine features derived from convolutions and context representations derived from transformers into a unified feature map; a decoding unit operationally coupled to the fusion unit and configured to generate a segmentation map corresponding to the tumor regions by incrementally increasing and reconstructing the spatial resolution; a boundary refinement unit configured to improve the delineation of tumor margins in the segmentation map;a processing unit that is operationally coupled with the preprocessing unit, the feature extraction unit, the transformer coding unit, the fusion unit, the decoding unit, and the boundary refinement unit, wherein the processing unit executes instructions stored in a memory unit to perform automated tumor boundary delineation; and a display interface configured to overlay the delineated tumor boundaries onto the radiological image data.

This application provides a method and system for image recognition and diagnosis of brain tumors in neurosurgery. First, the brain tumor image is preprocessed. The preprocessed brain tumor image is then decomposed into at least two feature component maps that complement each other in terms of structural and texture information. Next, each feature component map is sequentially subjected to a first-stage adaptive nonlocal mean filtering based on local statistical characteristics and a second-stage filtering based on structural correlation similarity measurement. The filtered feature component maps are then fused and reconstructed to obtain a feature-enhanced brain tumor image. This feature-enhanced brain tumor image is input into a pre-trained brain tumor recognition model, and the neurosurgical diagnosis results are obtained through the model. Using the scheme of this application, a two-stage adaptive filtering based on local scattering statistics and structural correlation measurement can be achieved, thereby enhancing tumor edge and texture features while suppressing ultrasound speckle, thus improving the accuracy of segmentation and diagnosis.

This invention provides a hepatocellular carcinoma (HCC) image classification method based on interference quantification and pathological priors, belonging to the field of medical technology. The method includes: acquiring HCC tumor images and dividing them into multi-region segmentation data, including a tumor core region, a tumor edge transition region, and a distal cirrhotic background region; calculating an interference index based on the gray-level and texture-related features of the HCC tumor images; performing adaptive feature selection and graded specific correction using a pre-constructed graded correction parameter library to obtain a corrected anti-interference tumor feature set; constructing a regularized loss function containing pathological constraints based on predefined cirrhosis and case association rules; training a model based on the anti-interference tumor feature set; and outputting the HCC image classification result. This invention solves the problem of difficult HCC image classification due to the blurred background boundaries and mixed features of tumors in HCC tumor images caused by varying degrees of cirrhosis in patients, as well as the scarcity of HCC tumor subtype samples.

A multi-axis tumor excision marker system is configured to deposit markers along multiple axes radially about a tumor to define a boundary of margin tissue for excision. A catheter may deploy a marker extension that extends to a margin tissue boundary for depositing the marker. The marker extension may be a shape memory material that extends radially outward from catheter opening or may be directed radially outward by a guide head. The radiopaque marker may be a phase change marker, such as an adhesive that changes from a liquid to a solid. A marker may be a discrete marker and may have an extension portion that extends from an anchor portion. An envelopment marker has a plurality of marker extensions that each extend along an offset axis from each other. A spiral maker includes a maker extension that spirals around the margin boundary tissue.

The present application relates to the technical field of image processing, in particular to a tumor margin intelligent recognition method based on HCR amplified fluorescence image, which aims at the problem that signal diffusion caused by defocus and margin recognition difficulty in HCR amplified fluorescence image, through evaluating the defocus degree of the image, calculating the fitting degree of the signal spot diffusion radius and the theoretically estimated radius, and combining the neighborhood difference analysis to generate the signal density distribution map, and finally recognizing different types of margins based on the signal density size. The present application can distinguish low-quality signals caused by defocus from real tumor signals, significantly improving the accuracy and reliability of tumor margin recognition.

The application relates to the field of intelligent medical treatment, in particular to a method, equipment and program product for predicting perineural invasion of infiltrative breast cancer. The method comprises the following steps: acquiring chest image data sets and labels of a breast cancer perineural invasion patient; inputting the chest image data sets and the labels into a classification model to be trained, the classification model comprising an encoding module, a parallel classification module, a region segmentation module and an edge segmentation module; inputting the chest image data sets and the labels into the encoding module to generate image features through feature encoding; inputting the image features into the classification module to generate an invasion classification result; inputting the image features into the region segmentation module to generate a tumor region mask; inputting the image features into the edge segmentation module to generate a tumor edge mask; repeatedly encoding, segmenting and classifying until a loss function is unchanged or a preset iteration number is reached; obtaining a classification model; and classifying perineural invasion of the infiltrative breast cancer through the classification model. The application has good clinical value.

The application relates to the technical field of image processors, and discloses a tumor incisal edge design method based on a skin local image. The method comprises the following steps: establishing a global space coordinate system associated with the skin of a patient; collecting a local image and a six-degree-of-freedom pose by using a handheld device integrated with a pose tracking function; projecting and fusing the image to a digital surface model in real time to generate a panoramic image; segmenting a tumor boundary by using a deep learning model, and generating a safe incisal edge path by outward expansion based on a geometric algorithm; and finally projecting the incisal edge to the skin surface in the form of structured light by using a built-in projection unit of the device. The application realizes high-precision and low-delay dynamic planning and visualized guidance of the tumor incisal edge, and improves the precision and efficiency of the operation.

This invention discloses a plasma electrode, an impedance measurement signal injection method, and a tumor edge recognition method, relating to the fields of electrosurgical instruments and biological tissue detection technology. To address the problems of traditional plasma surgery's inability to acquire tissue electrical properties in real-time and in situ, and its difficulty in accurately identifying tumor edges, this invention integrates two pairs of miniature measurement electrodes into the active electrode, with a ceramic-insulated microchannel on the back side; it uses multi-frequency signals superimposed at logarithmic frequency points of 1kHz to 5MHz, and injects them in parallel within 10ms after phase optimization using a genetic algorithm; it extracts the Cole relaxation frequency based on distributed relaxation time, and achieves rapid tumor edge recognition through offline pre-calculation and online processing. This invention achieves deep integration of ablation and sensing, enabling early determination of tissue type and improving the accuracy and safety of tumor resection.