58 results about "Biomedical image" patented technology

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

Presented herein are systems and methods for classifying biomedical images for executing operations. A computing system may identify a biomedical image of a slide with a biological sample obtained from a subject at risk of a condition; apply the biomedical image to a machine learning model; generate, based on applying the biomedical image to the ML model, a classification corresponding to the biomarker associated with the condition in biological sample on the slide; and execute an operation with respect to the slide for testing of the biological sample, in accordance with the classification for the biomedical image.

The invention relates to the technical field of biomedical image processing and analysis, in particular to an organ-like three-dimensional image enhancement segmentation method and system. The system comprises a data acquisition module, a light field analysis module, a physical enhancement module, a topological feature extraction module and a boundary evolution module. The system constructs a light transmission attenuation map by using deep learning, carries out voxel-level reverse illumination compensation, and carries out dynamic evolution segmentation in combination with a centripetal vector and a topological rejection potential energy field; the core is that digital transparency is realized based on a Beer-Lambert law inverse process, optical transmission physical degradation is eliminated, and brightness distribution of deep and shallow cells is consistent; according to the method, depth invariance of feature extraction is realized, and the problem that a subsequent segmentation algorithm is sensitive to depth change is effectively solved.

The invention discloses a brain age prediction method, system and device and a medium, and relates to the technical field of biomedical image analys.The method comprises the steps that firstly, precise local detail features representing the cerebral cortex and brain tissue are precisely captured, then the dependency relationship between local areas of different images is obtained based on a windowed multi-head self-attention mechanism, and the brain age prediction result is obtained; a channel attention mechanism is introduced, importance weights of different feature channels are learned, and the importance weights are applied to feature fusion, so that long-distance dependency relationships and fine structure features are accurately captured; and then based on a cross attention mechanism, guiding global detail features to focus on a region with rich local features and guiding the local detail features to focus on a most relevant local region so as to mine deeper detail features, and finally dynamically adjusting the contribution proportion of the local detail features and global context detail features in a final decision. And the features are fused into final features so as to perceive local detail features and global detail features in a deeper level.

The invention relates to a biomedical image feature fusion method based on a multi-scale heterogeneous hypergraph. The method comprises the following steps: S1, constructing a cell-level hypergraph model; s2, constructing a region-level hypergraph model; s3, constructing a sample-level hypergraph model; s4, transmission and fusion of cross-level hypergraph features: through a level graph neural network HGCN and a dynamic attention mechanism, transmission of cell-level hypergraph features-region-level hypergraph features-sample-level hypergraph features is carried out, global alignment is carried out, and global hypergraph features are obtained; and S5, constructing a histomorphological classification model by using the global hypergraph features, and outputting sample feature representation. According to the method, through multi-scale heterogeneous hypergraph modeling and cross-level dynamic feature fusion, the problems of single-scale characterization limitation and heterogeneous data high-order interaction bottleneck are solved, the cross-scale characterization capability of the biological tissue microenvironment is remarkably improved, the adaptability, generalization performance and analysis precision of a classification model to complex data scenes are enhanced, and the method is suitable for being popularized and applied. And a general framework is provided for complex biomedical analysis tasks.

Presented herein are systems and methods for detecting basal-cell carcinoma (BCC) in biomedical images of skin lesions. A computing system may identify, for a first subject at risk of BCC in a first lesion on a region of an epidermis: (i) a first biomedical image of an outer layer of the region on the epidermis of the first subject acquired in accordance with dermoscopy, and (ii) a plurality of second biomedical images of at least one inner layer in the region on the epidermis of the first subject acquired in accordance with reflection confocal microscopy (RCM). The computing system may apply the first biomedical image and the plurality of second biomedical images to a model architecture. The computing system may generate, based on applying to the model architecture, a score indicating a likelihood of BCC in the first lesion.

The invention relates to a high-fidelity super-resolution reconstruction method and equipment for microscopic images, and belongs to the technical field of industrial automation and machine vision. The image super-resolution reconstruction network constructed by the method sequentially comprises a shallow feature extraction module, a plurality of high-fidelity groups connected in series, and a residual connection and reconstruction module. Wherein a high-fidelity block in the high-fidelity group integrates a frequency sensing high-frequency branch and a global sensing low-frequency branch adopting four-direction two-dimensional selective scanning state space modeling, and image details and a global structure are cooperatively enhanced in a spatial domain and a frequency domain through adaptive fusion of a cross-gating fusion module. A pixel domain and frequency domain joint loss function is adopted for training. According to the method, the reconstruction fidelity and the detail recovery capability of the microscopic image are remarkably improved while the linear calculation complexity is kept, and the method is suitable for industrial detection, biomedical imaging and other scenes.

Presented herein are systems and methods for detecting labels in biomedical images. A computing system having one or more processors coupled with memory may identify, from a data source, a biomedical image having a first plurality of pixels in a first color representation. The computing system may convert the first plurality of pixels from the first color representation to a second color representation to generate a second plurality of pixels. The computing system may identify, from the second plurality of pixels, a subset of pixels having a color value satisfying a threshold value. The computing system may detect the biomedical image as having at least one label based at least on a number of pixels in the subset of pixels satisfying a threshold count. The computing system may store, in one or more data structures, an indication for the biomedical image as having the at least one label.

The application relates to the fields of biomedical image processing and intelligent control technology, in particular to an immune cell state analysis system based on an image processing technology; the system comprises feature extraction, state evaluation, decision generation and self-adaptive correction modules; the system extracts features by using a space-time graph neural network, the core of which is to calculate visual semantic entropy based on classification probability and feature response field, and to solve decision confidence weight by combining a cell motion diffusion index; accordingly, an AI regulation instruction and a conservative instruction based on a kinetic tolerance boundary are weighted and fused to generate a final instruction and to self-adaptively calibrate boundary parameters according to an observation error; by quantifying visual uncertainty and analyzing motion characteristics, the application effectively overcomes image blurring and non-biological interference, and significantly improves the recognition precision of active cells and the system robustness in a complex environment.

This invention relates to the field of biomedical image processing technology, and particularly to a cross-modal brain region image registration method based on differential homeomorphism. The method includes: registering the original T1-w image with an NMT-averaged standard template; registering the acquired cell architecture imaging and fluorescence imaging with the registered T1-w image; downsampling the fluorescence imaging and cell architecture imaging respectively; performing intensity correction; smoothing the cell architecture imaging / fluorescence imaging; re-downsampling the smoothed cell architecture imaging / fluorescence imaging; registering the T1-w image to the intensity-corrected cell architecture imaging / fluorescence imaging through affine transformation to obtain a deformation field; registering the T1-w D99 brain atlas to the cell architecture imaging / fluorescence imaging using the deformation field; and sampling the D99 brain atlas from the cell architecture imaging / fluorescence imaging to complete the registration. The advantages are: stronger robustness, higher accuracy, and no reliance on subsequent manual correction.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

The invention discloses a biomedical image segmentation method, system, device and medium, and relates to the technical field of medical images, and the method comprises the steps: collecting a biomedical image, and decomposing the biomedical image into a low frequency (LF) component and a high frequency (HF) component; a low-frequency (LF) component and a high-frequency (HF) component are respectively output to two parallel branches, a cross-branch pseudo-label guiding strategy is introduced, a pseudo-label generated by the low-frequency (LF) branch guides a high-frequency (HF) branch and captures a semantic context, and a pseudo-label generated by the high-frequency branch supervises the low-frequency branch to enhance the perception of fine-grained texture; and carrying out dynamic weight adjustment on a space quadrant of the feature map, selecting a better frequency branch as a final reasoning path, abandoning another branch, and generating a corresponding segmentation prediction result. According to the method, collaborative modeling of low-frequency semantics and high-frequency structures of the images is realized, so that the segmentation accuracy and generalization ability of the model under full-supervised and semi-supervised conditions are enhanced.

This invention relates to the field of biomedical image processing technology, and discloses a method and system for quantitative analysis of hypothalamic immunofluorescence images. The method includes: performing spectral unmixing on multispectral fluorescence microscopy images based on a nonnegative matrix factorization algorithm to obtain a clean signal distribution map; using Gaussian Laplace filtering and watershed transform to achieve cell detection and segmentation; performing affine and B-spline registration between slice images and standard brain atlases to generate regions of interest masks for neural nuclei; using a local background adaptive correction strategy to perform fluorescence quantification and positive determination; and calculating Pearson correlation coefficient and Manders overlap coefficient to achieve colocalization analysis. The system includes a spectral unmixing module, a cell detection and segmentation module, an atlas registration and region recognition module, a fluorescence intensity quantification module, and a colocalization analysis and statistical output module.

The application discloses a skeleton quality driven tubular structure segmentation closed loop optimization method and system, which is applied to the technical field of biomedical image processing and computer aided diagnosis, and the method comprises the following steps: using a trained segmentation model to infer medical volume data to obtain a segmentation mask; skeletonizing the segmentation mask to extract a skeleton graph; performing topological defect detection on the skeleton graph to obtain a structured defect report; reversely mapping three-dimensional coordinates in the report back to a voxel space, taking each defect point as a center, a preset radius as a range, and generating a defect density weight graph according to a severity score; and using the weight graph as a spatial weighting parameter of a loss function to optimize the segmentation model; and the application reversely maps the topological defects detected by skeletonization into a weight graph and integrates the weight graph into a loss function for closed loop iteration, realizes directional repair on high-occurrence areas such as fractures and false branches, and improves the conduction efficiency of segmentation improvement to skeleton quality.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

The invention provides an artificial intelligence-based system for generating personalized medical information through the integration of multi-modal data across pre-hospitalization, hospitalization, and post-hospitalization phases. The system employs encoder modules to process diverse input modalities, including audio recordings, video streams, biomedical images, text-based clinical records, and physiological signals. These encoded representations are integrated into a unified latent space using a large language model (LLM) trained on medical datasets comprising historical patient cases, anatomical knowledge, and treatment guidelines. The LLM enables cross-modal analysis to generate personalized outputs via decoder modules, which transform the latent space representation into actionable formats like text-based summaries, visualizations, audio explanations, and treatment suggestions. A key innovation is real-time intraoperative feedback via encoder-decoder modules detecting anatomical structures and deviations from standard protocols. The system also includes a federated learning module to aggregate model updates across medical centers while preserving patient privacy through deidentification protocols.

The invention relates to a chick sex identification model construction method, a chick sex identification method and a chick sex identification system. The chick sex identification method comprises the following steps: acquiring historical anus images at anus positions of multiple varieties of chicks, wherein the historical anus images are extracted frame by frame from a continuous video stream; preprocessing each historical anus image to obtain a historical image data set; constructing a basic chick sex identification model based on the historical image data set; and performing multi-task learning and transfer learning on the basic chick sex identification model to obtain a plurality of chick sex identification models corresponding to the plurality of varieties. According to the method, the common problem of feature loss in a high-noise biomedical image in a traditional method is solved, the problem of edge sawtooth caused by conventional clustering is avoided, and the chick sex identification accuracy of the chick sex identification model is remarkably improved.

The invention relates to a cell real-time detection method and device based on improved YOLOv12 and a storage medium, and is applied to the technical field of computer vision and biomedical image processing.The method includes the steps that an attention enhancement module AFE is arranged in a backbone network of an existing YOLOv12 model, the module combines channel attention with a space attention mechanism, and the attention enhancement module AFE is used for enhancing the attention of cells in the backbone network of the existing YOLOv12 model; a self-adaptive compression and spatial information aggregation strategy is adopted, so that the characterization capability of the model on key features such as tiny cells and fuzzy edges is effectively enhanced; an attention fusion module A2C2f is used in the neck network to replace an original standard C3k2 module at a target node, a fusion path of bottom-layer features and high-layer features is optimized, and collaborative optimization of feature extraction and multi-scale feature fusion is realized; according to the structure, the feature extraction capability of the model on tiny and fuzzy targets can be enhanced, and the fusion efficiency of multi-scale features is improved, so that the detection precision and the recall rate in complex scenes such as dense cells and overlapping are remarkably improved, and the omission ratio is effectively reduced.

The present application relates to the technical field of biomedical image processing and analysis, in particular to a kind of organoid three-dimensional image enhancement segmentation method and system;Contain data acquisition, light field analysis, physical enhancement, topological feature extraction and boundary evolution module;System utilizes deep learning to construct light transmission attenuation atlas, carries out voxel-level reverse light compensation, and carries out dynamic evolution segmentation in combination with centripetal vector and topological repulsive potential field;Its core is based on inverse process of Beer-Lambert law realizes digital transparency, eliminates light transmission physical degradation, so that the brightness distribution of deep and shallow layer cells is consistent;The present application realizes the depth invariance of feature extraction, effectively solves the problem that subsequent segmentation algorithm is sensitive to depth change.

The present application relates to the technical field of biomedical image processing, and discloses a multi-scene video intelligent clipping method and system, comprising the following steps: obtaining time-synchronized original microscopic video stream and structured experimental operation log; for each operation event, constructing an event neighborhood reference window in the original microscopic video stream and performing spatial registration to generate a locally video segment after spatial correction; generating a spatial attention weight mask according to the operation space coordinate vector, calculating a structure disturbance response value sequence of each frame of the local video segment in combination with a structural similarity index, and obtaining a phase significance score sequence through smoothing filtering; generating an original candidate segment based on the discrete first-order derivative of the phase significance score sequence and baseline period statistical characteristics; according to the preset priority weight of the operation action type label corresponding to the candidate segment, performing cutting and transition processing on the time overlapping segment, and outputting an abstract video file; and generating a metadata index file associated with the abstract video file.

The invention relates to the technical field of biomedical image processing and intelligent control, in particular to an immune cell state analysis system based on an image processing technology. A feature extraction module, a state evaluation module, a decision generation module and an adaptive correction module are included; the system uses a space-time diagram neural network to extract features, the core is to calculate visual semantic entropy based on classification probability and a feature response field, and a decision confidence coefficient weight is solved in combination with a cell motion diffusion index; performing weighted fusion on an AI regulation and control instruction and a conservative instruction based on a dynamic tolerance boundary, generating a final instruction, and adaptively calibrating boundary parameters according to an observation error; according to the method, by quantifying visual uncertainty and analyzing motion characteristics, image blurring and non-biological interference are effectively overcome, and the recognition precision and system robustness of active cells in a complex environment are remarkably improved.

This invention discloses a GPU-based neuronal skeleton extraction method based on topology-simplified trunk growth, relating to the fields of neuroinformatics and 3D biomedical image processing. The method includes: threshold filtering and denoising of the input volume data, and retaining the largest connected component based on 3D connected component labeling to remove isolated noise; given the cell body center coordinates, determining the cell body region through spherical expansion and removing it from the foreground to highlight neural protrusions such as dendrites and axons; after obtaining the coarse skeleton, using GPU to perform topology simplification, and constructing a path scoring function combining path length and average normalized brightness, iteratively selecting and retaining the trunk path with the best score, enhancing trunk continuity and suppressing pseudo-branches caused by noise and breaks. This invention can achieve robust skeleton extraction in 3D data with noise interference and uneven brightness, and is suitable for applications such as neuronal morphology reconstruction and quantitative analysis.

This application relates to the field of biomedical image processing, providing a method and corresponding product for segmenting stem cell electron microscopy images based on fractional-order level sets. The method includes: acquiring electron microscopy images of adherent stem cells; sequentially performing grayscale processing and Gaussian filtering on the acquired electron microscopy images to remove noise, obtaining a denoised image; constructing a level set function based on fractional-order calculus theory, and segmenting the stem cell region from the denoised image through iterative optimization, obtaining a segmentation result; performing connected component analysis on the segmentation result to remove small noise regions and optimize the segmentation effect. The technical solution of this application, based on the fractional-order level set method, effectively improves the accuracy and robustness of stem cell electron microscopy image segmentation through image preprocessing and iterative optimization.

This invention discloses a method and system for in vitro assessment of renal biopsy tissue quality based on virtual special staining, belonging to the field of biomedical image processing technology. The method includes: acquiring and scanning undewaxed mounted sections to be assessed to obtain unstained whole-section images; inputting the unstained whole-section images into a pre-trained hybrid contrast virtual staining generation model to generate virtual special staining images with specific staining textures; the hybrid contrast virtual staining generation model is obtained through optimization training based on a total loss function, which includes adversarial loss, L1 loss, and PatchNCE block noise contrast estimation loss to maximize the mutual information between the input and generated images at corresponding spatial locations; inputting the virtual special staining images into a target detection network to identify glomerular structures and count the total number of glomeruli; and outputting the suitability assessment result of the undewaxed mounted sections based on the comparison result between the total number of glomeruli and a preset threshold. By scanning undewaxed mounted sections, the traditional dewaxing and rehydration steps are eliminated, improving efficiency; and the virtual silver staining technology significantly enhances the ability to identify glomeruli.

This invention relates to the technical field of biomedical imaging and discloses a cross-scale photoacoustic imaging method based on a liquid lens, comprising the following steps: S10: constructing a cross-scale photoacoustic imaging system based on a liquid lens; S20: pulsed laser emitted by a laser emitting module is incident on a liquid permeation adjustment module, and then, after passing through a laser excitation module, it irradiates the sample and excites a photoacoustic signal; S30: changing the focal length of the liquid permeation adjustment module so that the pulsed laser emitted from the laser excitation module achieves a strong focusing mode (corresponding to OR-PAM) and a weak focusing mode (corresponding to AR-PAM) on the sample; S40: the photoacoustic signal is received by a signal acquisition module, and the received photoacoustic signal is calculated to generate an image. By changing the focal length of the liquid lens, switching between OR-PAM and AR-PAM photoacoustic imaging is achieved based on a single optical path; this cross-scale photoacoustic imaging method based on a liquid lens involves no optical fiber, no beam splitting, and no beam cutting, making it simple, uncomplicated, and low-cost.

Presented herein are systems and methods of determining tumor responses in brains from administering radiotherapy. A computing system can: identify a plurality of biomedical images of a brain of a subject; perform an image registration on a first biomedical image with a second biomedical image to determine a plurality of translation parameters; generate a third biomedical image using the second biomedical image in accordance with the plurality of translation parameters, detect using an image segmentation model, (i) a first segment identifying a first region within the third biomedical image and (ii) a second segment identifying a second region within the second biomedical image; and determine a metric indicating a degree of responsiveness of a tumor in the subject to the administration of the radiotherapy to the brain, based on the first segment and the second segment.

A neural network architecture and method for analysis of time series images from an image source employs a 3D-UNet convolutional neural network (CNN) configured to receive the time series images and generate spatiotemporal feature maps therefrom. Multiple sub-convolutional neural network output prongs based on an SRNet architecture receive the feature maps and simultaneously generate inferences for image segmentation, regression of values, and multi-landmark localization.

The invention relates to a water-soluble manganese-doped titanium dioxide nanorod as well as a preparation method and application thereof, and belongs to the technical field of nano material preparation. The preparation method of the water-soluble manganese-doped titanium dioxide nanorod comprises the following steps: adding titanium tetrachloride into oleic acid to prepare a titanium oleate compound precursor; dissolving manganese oleate in oleic acid, carrying out ultrasonic treatment, adding the titanium oleate compound precursor, and continuously carrying out ultrasonic treatment to obtain a titanium oleate and manganese oleate compound precursor; the preparation method comprises the following steps: taking a compound precursor of titanium oleate, adding a mixed system of oleylamine, oleic acid and octadecene, injecting the compound precursor of titanium oleate and manganese oleate to obtain an oil-soluble manganese-doped titanium dioxide nanorod, and carrying out water-soluble surface modification through sodium citrate to obtain the water-soluble manganese-doped titanium dioxide nanorod. The nanorod prepared by the invention can be used as an excellent T1 magnetic resonance molecular imaging contrast agent, and has important application value and wide application prospect in the biomedical imaging fields of tumor detection, angiography and the like.

The application discloses a biomedical image segmentation method, system, device and medium, and relates to the technical field of medical images, and comprises the following steps: collecting a biomedical image, and decomposing the biomedical image into a low-frequency LF component and a high-frequency HF component; the low-frequency LF component and the high-frequency HF component are respectively output to two parallel branches, a cross-branch pseudo-label guiding strategy is introduced, the pseudo-label generated by the low-frequency LF branch guides the high-frequency HF branch, the semantic context is captured, the pseudo-label generated by the high-frequency branch supervises the low-frequency branch, and the perception of fine-grained texture is enhanced; the spatial quadrant of a feature map is subjected to dynamic weight adjustment, a better frequency branch is selected as a final reasoning path, another branch is discarded, and a corresponding segmentation prediction result is generated. The application realizes collaborative modeling of low-frequency semantics and high-frequency structures of an image, and further enhances the segmentation accuracy and generalization ability of a model under full supervision and semi-supervised conditions.

The application relates to a micro brain imaging device for dual-color fluorescence and an imaging method thereof, and relates to the technical field of biomedical imaging devices.The device comprises a connected shell and a lens end cover, and an imaging light path and an illumination light path are sequentially arranged inside the shell and the lens end cover along an imaging optical axis.The imaging light path comprises a lens group, a first dichroic mirror, a first filter and a fifth lens which are sequentially arranged.The lens group is composed of a first lens, a second lens, a third lens and a fourth lens which are sequentially arranged.The illumination light path comprises a first light source, a second light source, a second dichroic mirror and a sixth lens.The first light source and the second light source form a collimated light beam after being combined by the second dichroic mirror, and the collimated light beam is shaped by the sixth lens and then projected to an object plane through the first dichroic mirror.The application has the effects of simultaneously monitoring different types of neural signals or cell groups, distinguishing different states or functional modules of neurons, and improving the resolution and accuracy of data.