10 results about "Nodule detection" patented technology

This invention relates to the field of medical image processing, specifically to a multimodal medical image fusion system and method, comprising a data preprocessing module, a domain manifold embedding module, a hierarchical adaptive domain alignment network module, a multimodal feature fusion module, and a diagnostic decision module. The system establishes a topological correspondence between EBUS ultrasound, OCT tomography, and DWI functional images through a multidimensional domain manifold embedding structure; employs a hierarchical adaptive domain alignment network to achieve multi-scale feature extraction and precise alignment; designs a topology-preserving multimodal feature fusion mechanism to ensure the complete preservation of key diagnostic information from each modality during the fusion process; and utilizes a dual-path cross-validation decision strategy to improve the stability and reliability of diagnosis. The system inputs the domain-aligned multimodal features into a Transformer network and generates nodule detection and benign / malignant classification results through dual-threshold judgment, achieving accurate and non-invasive diagnosis of small lesions such as pulmonary nodules and early gastrointestinal lesions.

The application relates to the technical field of image analysis, in particular to a respiratory system lung nodule tumor cell detection method and system based on image feature fusion and a storage medium, which comprises the following steps: constructing a two-dimensional pixel gray array of a chest CT image and performing edge positioning, constructing a closed image boundary through gray difference analysis and gradient direction continuity, extracting a lung nodule candidate region image in the closed region, establishing pixel adjacency relations in horizontal, vertical and diagonal directions, constituting a direction difference image set and extracting a continuous texture region with a closed structure, generating a lung nodule edge contour connection image through spatial relationship mapping and pixel connection extension, and finally performing boundary fusion and pixel structure recombination to form a target detection image. In the application, the direction gray difference construction and boundary closure analysis are combined, the connected path judgment and pixel aggregation processing are fused, the texture structure concentration and image recognition definition are effectively enhanced, and the accuracy and readability of lung nodule detection are improved.

This invention discloses a method for constructing a lung weight index based on chest CT images and its application. The method includes: Step 1, low-dose CT image acquisition and preprocessing; Step 2, automatic segmentation of the lung and lung lobes; Step 3, calculation of the average density of lung lobes and conversion to physical density; Step 4, calculation of lung lobe volume; Step 5, calculation of lung lobe weight and whole lung weight; Step 6, construction of the lung weight index; Step 7, risk assessment based on the lung weight index. This method can be applied to practical scenarios such as lung cancer screening, health checkups, and image-assisted diagnosis. This invention comprehensively reflects the state of lung tissue, blood vessels, and interstitium through the lung weight index, providing a more holistic quantitative description. Furthermore, it does not require nodule detection as a necessary prerequisite, making it suitable for individuals with few nodules, small volume, or those who have not yet formed a definite mass, thus better meeting the practical needs of early lung cancer screening.

This application proposes a method and application for constructing a thyroid nodule detection model based on mid-frequency feature interaction. The method includes: acquiring multiple training images, wherein the training images are thyroid ultrasound images annotated with thyroid nodules; constructing a thyroid nodule detection architecture consisting of a backbone network, a feature fusion network, and a classification network connected sequentially; and training the thyroid nodule detection architecture using multiple training images to obtain the constructed thyroid nodule detection model. This scheme uses dilated convolutional units with depthwise separable convolutions at different dilation rates for feature processing, which can aggregate mid-range context while preserving details of small nodules. Furthermore, the mid-frequency feature interaction unit achieves efficient coupling between local details and mid-range semantics, thereby enabling the thyroid nodule detection model to achieve better classification performance.

The present application relates to the technical field of medical image processing, in particular to a multi-center lung CT nodule precision detection system based on federated learning. The client includes: a physical parameter estimation unit, which estimates the imaging physical parameters of the CT image based on the preset imaging physical prior knowledge base; a physical-semantic decoupling unit, which decomposes the CT image into physical invariant semantic features and device-specific physical encoding based on an invertible neural network; a nodule detection unit, which detects lung nodules based on the physical invariant semantic features. The central server includes: a global semantic anchor library, which stores typical nodule semantic feature vectors uploaded by each client; a hierarchical aggregation unit, which performs federated weighted aggregation on the semantic encoder parameters of each client, and does not perform cross-center aggregation on the physical adapter parameters. The feature incomparability problem caused by the difference of imaging devices in multi-center data is solved, and cross-center semantic alignment is realized under the premise of protecting data privacy.

The present application relates to the technical field of lung nodule detection, in particular to a lung nodule detection method and system based on adaptive multi-scale deformable attention. The method comprises the following steps: acquiring a clinical lung nodule dataset; performing data preprocessing on the acquired dataset; constructing a deep network model based on AMDA-YOLO; training the deep network model based on AMDA-YOLO using a linear warm-up strategy; performing lung nodule detection using the trained model; and outputting the detection results. The detection system constructed by the present application has excellent cross-data domain generalization capability and clinical practical value.

The application discloses a thyroid nodule detection method based on dynamic blur adaptive fusion, relates to the technical field of medical images, and takes into account the problems of real-time performance and image quality blur; the method does not suppress noise by regarding blurred images as noise interference, but learns and understands the blur structure, and the process is realized by outputting clear features and blur features through a double-branch perception network; a clear-blur switching unit is designed according to the change of the scanning speed and the average Laplacian variance of the image, the strategy can be adaptively adjusted according to the blur degree of the input image, and the clear features and the blur features are dynamically mixed. Moreover, the application is a plug-and-play component, and can be organically embedded in mainstream target detection frameworks, such as the YoLO series and the DETR series.

The application discloses a liver nodule detection frame and benign and malignant probability prediction method and device based on CT and MRI multi-modal images, and relates to the technical field of medical image processing, and aims to improve the accuracy of liver nodule detection frame and benign and malignant probability prediction.

This invention relates to the technical field of thyroid ultrasound image detection, and provides a method and system for thyroid ultrasound image nodule detection based on pattern recognition. The method includes: identifying a decisive local region that plays a crucial role in determining thyroid nodules in ultrasound image data based on fine feature data and stable feature data, and performing depth analysis to obtain depth analysis results; when an acoustic shadowing region exists within the decisive local region, inferring the structural characteristics of the acoustic shadowing region based on ultrasound imaging principles and surrounding tissue information, and performing structural feature enhancement processing on the decisive local region to update the depth analysis results; and generating and outputting auxiliary interpretation information based on the fine feature data, stable feature data, and depth analysis results. This invention improves the accuracy and stability of auxiliary interpretation information for thyroid ultrasound image nodule detection.

This invention discloses a dual-branch pulmonary nodule detection system and method combining domain incremental learning and low-dose CT. The continuous domain adaptive detection module of the system includes: a historical domain prior detection model, a current domain adaptive detection model, and a multi-scale inter-domain knowledge transfer framework. Both the historical domain prior detection model and the current domain adaptive detection model embed a channel space collaborative cross-domain optimization module. This invention effectively mines the features of small lesions and cross-domain general anatomical knowledge in the high-noise environment of low-dose CT, solving the catastrophic forgetting and domain drift problems faced by traditional deep learning models in multi-center applications. Compared with existing technologies, this invention can construct a pulmonary nodule detection system with continuous evolution capabilities and strong generalization based on incremental low-dose CT data streams without storing historical private data, thereby achieving efficient, robust, and accurate automated screening of data from different medical institutions and different imaging equipment.