9 results about "Lesion analysis" patented technology

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

This invention discloses a kidney pathology image processing method integrating multi-tissue segmentation and quantitative lesion analysis, belonging to the field of medical image processing technology. The method includes the following steps: acquiring and preprocessing PAS-stained whole-slice images of kidney pathology; fine-tuning the segmentation model using an unsupervised domain adaptive strategy to address batch-to-batch staining differences; inputting the preprocessed image into a multi-class semantic segmentation neural network to obtain tissue segmentation results; training the network based on pixel-level annotations, employing a Class-Token mechanism, encoder-decoder architecture, and multi-scale feature fusion, and optimizing the Dice loss and binary cross-entropy loss based on joint weighting of categories and boundaries; performing statistical analysis based on the segmentation results and outputting quantitative analysis results. This invention provides an objective, reproducible, and intelligent auxiliary tool for the accurate assessment and large-scale clinical research of chronic kidney disease.

The present application belongs to the technical field of image processing, and discloses an intelligent auxiliary processing system for abdominal cavity operation images, which comprises a data acquisition module, a data processing module, an intraoperative lesion diagnosis module, an intraoperative lesion analysis module, a clinical decision module and a scheme regulation module. The data acquisition module is used for acquiring laparoscope 3D optical molecular image data, RAL imaging data and device operation data. The data processing module is used for obtaining a comprehensive feature data set by processing the acquired laparoscope 3D optical molecular image data, RAL imaging data and device operation data. The intraoperative lesion diagnosis module is used for constructing an intraoperative lesion diagnosis model and predicting an optical feature index. The intraoperative lesion analysis module is used for comparing the predicted optical feature index with a preset optical feature index threshold value and judging whether there is a lesion. The clinical decision module is used for constructing a lesion degree prediction model and predicting a lesion severity. The scheme regulation module is used for implementing a corresponding tumor treatment scheme according to the lesion severity.

The application relates to the technical field of medical image processing, and particularly discloses a cavity image real-time lesion recognition and boundary segmentation system. A cross-sectional image sequence in a cavity is acquired through an interventional imaging catheter, and after shared features are extracted by using a deep network, pixel-level tissue boundary segmentation and overall lesion property recognition are synchronously performed. The core lies in that a dynamic gradient coordination mechanism is introduced, gradient direction conflicts between the segmentation and recognition double tasks are detected and resolved in real time during the training process, and adaptive projection transformation based on the task convergence state is used to fuse the gradients, so that the shared network can learn balanced features which are optimal for both tasks; the method realizes end-to-end real-time processing from image input to synchronous output of the segmentation and recognition results, and significantly improves the synergy, accuracy of lesion analysis and real-time auxiliary efficiency of clinical operation navigation.

This application relates to the field of medical image processing technology, and discloses a method and system for lesion analysis of SPECT and spectral CT fusion images. The method includes acquiring SPECT and spectral CT images; identifying at least one anatomical feature point in the spectral CT image and identifying a functional feature point corresponding to the anatomical feature point in the SPECT image; calculating the first spatial coordinates of the anatomical feature point in the spectral CT image and the second spatial coordinates of the functional feature point in the SPECT image; determining the deformation parameters of a local region in the SPECT image based on the difference between the first and second spatial coordinates; and performing spatial transformation processing on the functional distribution features of the local region in the SPECT image to spatially align the processed functional distribution features with the anatomical features in the spectral CT image, actively eliminating image misalignment caused by physiological motion and other factors, and achieving alignment of functional information with anatomical structures.

This invention discloses an oral ablation detection method and system, belonging to the field of oral detection technology, including: S1, oral tissue sampling; S2, motion compensation; S3, image reconstruction; S4, lesion analysis; S5, ablation planning; S6, ablation execution. This invention utilizes phase-amplitude joint displacement estimation, extracting depth-direction micro-displacement using phase difference and lateral displacement using amplitude centroid shift. This effectively compensates for non-rigid tissue peristalsis caused by swallowing and tongue movements without relying on external markers or high-frequency frame rates, ensuring that the subsequently reconstructed three-dimensional image and ablation target area localization are not distorted due to motion, thus improving the targeting consistency between detection and treatment. Texture analysis at different scales is applied along the oral mucosal epithelium, basement membrane zone, and lamina propria, and depth-adaptive weights are assigned based on the pathological sensitivity differences of each layer, highlighting the probabilistic response of early cancerous areas.

Embodiments of the present application relate to the technical field of medical data processing, in particular to a multi-modal medical image data processing method and device. The method comprises: inputting multi-modal data of a target lesion into a trained multi-modal medical data processing model; performing multi-scale basic visual feature extraction on medical image data through a basic visual feature extraction network to obtain a multi-scale basic visual feature set; capturing a spatial structure of the multi-scale basic visual feature set through a structured image feature extraction network to obtain a structured image feature; performing semantic feature extraction on medical text data through a text feature extraction network to obtain a text semantic feature; and performing prior weighting fusion on the structured image feature and the text semantic feature through a cross-modal feature fusion network to obtain a matching probability of the multi-modal data and the target lesion. The technical solution of the present application can improve the reliability of lesion analysis results.

The application discloses a lung multi-anatomy analysis and three-dimensional reconstruction method, which comprises the following steps: acquiring and preprocessing chest CT scan images to obtain input tensors; identifying and outputting airway masks and lung blood vessel masks in the input tensors by using a connected structure segmentation network; identifying and outputing lung nodule masks in the input tensors by using a micro-lesion analysis network; determining spatial physical coordinates of foreground voxels of each mask in the chest CT scan images, and determining unique anatomy class labels of each voxel by using a dynamic clinical risk priority conflict resolution strategy to obtain a multi-anatomy voxel set; and sampling and reconstructing each anatomy based on an adaptive manifold sampling strategy based on morphological characteristics to obtain reconstructed lung multi-anatomy. The method can clearly show the spatial relationship among airways, blood vessels and lesions in a unified three-dimensional coordinate system, and provides an intuitive and accurate digital model for clinical diagnosis and treatment.

This invention relates to the field of medical imaging lesion analysis technology, specifically a multi-source point cloud fusion system and method for analyzing knee and lumbar spine lesions. The system includes: acquiring static medical imaging scan data and dynamic motion capture data of the target area; reconstructing anatomical structure point clouds from the static data in three dimensions; generating motion trajectory point cloud sequences through spatiotemporal registration and trajectory extraction of the dynamic data; establishing a spatial mapping field between the two and transferring motion vectors; driving the anatomical structure point cloud to deform under physical constraints to simulate and generate dynamic skeletal point clouds; calculating local curvature changes to form a curvature spatiotemporal evolution map; separating physiological low-frequency and pathological high-frequency components through multi-scale frequency domain decomposition; extracting abnormal vibration mode features and comparing them with a standard lesion knowledge base; and outputting a qualitative description of potential knee or lumbar spine lesions. This invention integrates static anatomical and dynamic motion data, accurately distinguishing between physiological and pathological vibrations, and improving the accuracy of lesion analysis.