130 results about "Biomedical image" patented technology

The invention discloses a rapid polarized light acoustic imaging device which comprises an excitation source, a light beam shaping module, a rapid polarization modulation module, an energy feedback module, a rapid scanning module and an acquisition processing module. And receiving photoacoustic signals corresponding to the exciting light in four different polarization states for calculation, and extracting anisotropy information of the sample. Compared with multiple times of scanning a sample or modulating the polarization state by rotating a half-wave plate, the problems of dislocation of multiple times of polarization acquisition points and false positive calculation are avoided, meanwhile, the time interval generated by continuously modulating the laser pulses is short, and energy fluctuation among the four pulses and errors caused by fluctuation are reduced. According to the invention, the limitation of the precision and speed of polarized light acoustic microscopic imaging is overcome, the microstructure characteristics in the target sample can be rapidly, accurately and highly sensitively detected, and the method has great application prospects in biomedical imaging and high-precision material detection.

Presented herein are systems and methods for semantic image retrieval. A computing system may identify a first biomedical image. The computing system may apply an image retrieval model to the first biomedical image. The image retrieval model may have a convolution block having a first plurality of parameters to generate a feature map using the first biomedical image. The first plurality of parameters may be transferred from a preliminary model. The image retrieval model may have an encoder having a second plurality of parameters to generate a first hash code for the first biomedical image based on the feature map. The computing system may select. from the plurality of second biomedical images corresponding to a plurality of second hash codes, a subset of second biomedical images using the first hash code. The computing system may provide the subset of second biomedical images identified using the first biomedical image.

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.

The invention belongs to the technical field of biomedical image processing and analysis, and particularly discloses a chromosome abnormality detection method based on image analysis, which comprises the following steps: obtaining a user chromosome microscopic image set and extracting a multi-modal feature data set for processing to obtain abnormal chromosome feature data of an abnormal tracking area; and performing collection processing to obtain an abnormal risk assessment index, performing correction in combination with abnormal identification coefficients of a plurality of historical similar abnormal samples to obtain an abnormal risk correction index, and performing assessment processing according to an obtained clinical detection data set corresponding to the abnormal tracking area in combination with genetic background feature data to obtain an abnormal tracking area genetic risk judgment index. And comparing the abnormal risk correction index with the abnormal risk correction index to obtain an abnormal evaluation matching degree coefficient, and verifying an abnormal evaluation result through threshold comparison, so that efficient identification and accurate evaluation of various complex chromosome abnormality types are realized, and comprehensive risk evaluation is performed in combination with genetic background information and clinical data to improve the reliability of a detection result.

The invention relates to biomedical images, in particular to a digital pathological image feature extraction system and method, solves the problem that pathological image features cannot be fully extracted in the prior art, and performs feature fusion on tumor infiltration information features and tumor spatial heterogeneity information features extracted from pathological images. The fusion result is used as the pathological image features of the pathological image, and the obtained pathological image features can reflect the pathological image features more comprehensively and can be used for tumor subtype classification tasks, survival prediction tasks and the like.

The invention belongs to the field of biomedical image processing, and relates to a blastocyst-stage embryo morphological quality evaluation method based on multi-modal information, which comprises the following steps: acquiring an embryo microscopic image and clinical data of a patient, and acquiring a trained embryo morphological quality evaluation model, based on the trained embryo morphological quality evaluation model, performing knowledge distillation according to the embryo microscopic image and clinical data of the patient to obtain a student model; obtaining an embryo microscopic image and clinical data of a patient, and inputting the embryo microscopic image and the clinical data of the patient into the student model to obtain an evaluation result; the embryo morphological quality evaluation model comprises a multi-modal feature extraction module, a comparison module, a multi-modal feature fusion module and a multi-task embryo quality rating module; according to the method, the comparison learning mechanism is utilized to align the embryology evaluation text information with the image features, so that the evaluation accuracy is improved.

The invention discloses a lightweight multi-scale biomedical image segmentation method, and relates to the technical field of medical image processing, and the method comprises the steps: collecting and obtaining a reference position medical image for a target operation task; performing feature importance and feature dominance analysis in combination with key texture feature information of a target operation, and calibrating a datum positioning reference point; real-time image tracking segmentation is carried out, rapid positioning alignment and multi-scale feature extraction are carried out on continuous medical images, and a multi-scale feature map is generated; introducing a feature fusion mechanism, and performing layer-by-layer fusion on the multi-scale feature map to obtain a fused feature map; and obtaining an image segmentation result. The technical problems that existing biomedical image segmentation is difficult to adapt to complex and diversified image segmentation tasks, so that the image segmentation precision is low, and the reliability and safety of surgical navigation are reduced are solved, high-precision image segmentation is realized, and the technical effect of improving the reliability and safety of surgery is achieved.

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 discloses a multi-fluorescence immunohistochemical image intelligent analysis method based on deep learning. The method comprises the following steps: acquiring image data through image acquisition equipment, and performing data integrity verification, format conversion and the like to obtain a standard image data set; performing noise removal and brightness equalization preprocessing on the image based on a preset rule, and extracting image features by using a deep learning model; and finally carrying out region segmentation and object identification according to the features. According to the method, full-process automation of multiple fluorescence immunohistochemical images from collection to analysis is realized, and the accuracy, efficiency and reliability of image analysis are effectively improved through standardized processing, adaptive preprocessing and deep learning feature extraction and analysis of image data; powerful technical support is provided for biomedical research and clinical diagnosis, and the method has important application value.

Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.

The invention relates to the field of biomedical image processing, and discloses an organ-like three-dimensional image segmentation method and system and electronic equipment, and the method comprises the steps: carrying out the slicing processing of the three-dimensional image data of an organ, converting the data into a two-dimensional image sequence, and carrying out the fine preprocessing of each two-dimensional slice, so as to eliminate the noise and enhance the image features; and then, adaptive time sequence enhancement processing is carried out, effective extraction and enhancement of time sequence features are carried out on the preprocessed two-dimensional image sequence, and the utilization rate of image information is improved. On this basis, a Kolmogorov-Arnold theory and a UNet architecture are combined to construct a high-performance image segmentation model, and deep training and testing of the model are carried out through an organ-like three-dimensional image data set. And finally, inputting the enhanced two-dimensional image sequence into the trained model to realize high-precision segmentation of the organoid. The method has the advantages of high segmentation accuracy, high processing efficiency and the like, and has relatively high practical value and application prospect.

Presented herein are systems and methods for determining scores related to metastatic recurrence of cancers in subjects using multimodal machine learning (ML) architectures. A computing system may obtain, for a subject at risk of recurrence of cancer, a dataset comprising at least one of: (i) a biomedical image of a tissue sample from an organ associated with the cancer or (ii) a text report identifying a plurality of characteristics of a tumor associated with the cancer. The computing system may apply an ML architecture to at least one of the biomedical image or the text report of the dataset. The computing system may determine, based on applying the ML architecture, a score indicating a likelihood of recurrence associated with cancer in the subject. The computing system may generate a classification of the subject for administration of a therapy for the cancer, in accordance with the score.

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 schizophrenia auxiliary diagnosis method based on spatio-temporal dynamics, and belongs to the technical field of biomedical image mode recognition. A functional brain network image is obtained by f-MRI, and a space-time dynamic model is constructed by means of a Markov chain, so that an individual functional brain network convergence mode and a maximum information flow transmission mode between brain networks can be extracted. Three corresponding index quantization function brain network spatio-temporal dynamic modes are provided, and the three corresponding index quantization function brain network spatio-temporal dynamic modes are a network stationary probability vector, a network optimal step number mean value matrix and a network direct connection strength matrix. According to the method, an effective auxiliary diagnosis model is constructed for three proposed functional brain network spatio-temporal dynamic indexes by means of a statistical test method and a logistic regression algorithm, so that accurate quantitative calculation of the probability of suffering from schizophrenia of an individual is realized, and a powerful technical support is provided for early diagnosis and intervention of schizophrenia; the kit fills the blank of the existing diagnosis technology in the field, and has remarkable clinical application value and scientific significance.

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 classifying features from biomedical images. A computing system may identify a first portion corresponding to an ROI in a first biomedical image derived from a sample. The ROI of the first biomedical image may correspond to a feature of the sample. The computing system may generate a first embedding vector using the first portion of the first biomedical image. The computing system may apply the first embedding vector to a clustering model. The clustering model may have a feature space to define a plurality of conditions. The clustering model may be trained using a second embedding vectors generated from a corresponding second portions with at least one of a plurality of image transformation. The computing system may determine a condition for the feature based on applying the first embedding vector to the clustering model.

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 invention relates to the technical field of biomedical imaging, and discloses a fluorescence image and magnetic resonance image registration method comprising the following steps: preprocessing a fluorescence image, and converting the fluorescence image into a magnetic resonance image modal through modal migration; preprocessing the magnetic resonance image; registering the fluorescence image converted into the magnetic resonance mode to the preprocessed magnetic resonance image, and obtaining a deformation field at the same time; the fluorescence image which is subjected to modal migration and is registered with the magnetic resonance modal is further registered to a magnetic resonance standard template, and a deformation field is obtained at the same time; mapping the fluorescence image to a magnetic resonance standard space through the two deformation fields; through two reverse deformation fields, an atlas in a magnetic resonance standard space is inversely mapped to a fluorescence image. According to the method, the registration difficulty between the fluorescence image serving as the high-resolution optical mesoscopic image and the macro-scale magnetic resonance image and the difficulty of traditional cross-modal registration are overcome, and the registration accuracy is improved.

The invention provides a three-dimensional vascular structure recovery device and method fusing prior information, and relates to the technical field of biomedical image processing. Comprising a data acquisition module, a data extraction module and a complex blood vessel missing structure recovery module. The method comprises the following steps: firstly, acquiring SR-CT data of a blood vessel network, and then carrying out image segmentation on the blood vessel network to obtain a binary mask; decomposing the complex blood vessel into a series of single blood vessel images according to an image segmentation result of the blood vessel network; according to a series of single blood vessel images obtained through decomposition, selecting a single blood vessel image containing a missing structure through a connected domain analysis method; a spatial transformation network based on shape prior is constructed, and a missing structure of a single blood vessel is recovered; and finally, recombining the single blood vessel image, namely the blood vessel image without the missing structure and the blood vessel image with the restored missing structure to generate a complete blood vessel network, thereby realizing missing structure restoration of the complex blood vessel network.

The present invention discloses an aberration compensation method and device, the method comprising: step S1, compensating and superimposing a conjugate wavefront aberration phase on an initial input phase to obtain a pre-compensated phase; step S2, superimposing the pre-compensated phase through the wavefront aberration phase distortion of an optical element itself to obtain a compensated phase, and subjecting the compensated phase to Fourier transformation to obtain aberration-compensated Fourier surface intensity; the aberration-compensated Fourier surface intensity information has a low bit error rate and a good reconstruction effect; it is a numerical compensation method without the need to add additional optical components; thereby reducing the number of optical elements used in an optical system and reducing the complexity of the optical system, providing good technical support for aberration compensation in, for example, the field of holographic storage, and having good application prospects in, for example, the field of holographic data storage research; it can be applied to the fields of holographic storage, biomedical image processing, digital holographic microscopy, etc.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

The present disclosure is directed to systems and methods for determining measures of tumors from biomedical images. A computing system can receive (i) a first biomedical image derived via a computed tomography (CT) scan of an organ associated with a tumor in a subject and (ii) a second biomedical image derived via a positron emission tomography (PET) scan of the organ. The computing system can determine, from the first biomedical image, a region of interest (ROI) corresponding to the organ associated with the tumor in the subject. The computing system can identify a portion in the second biomedical image corresponding to the ROI. The computing system can generate a plurality of measures of the tumor in the subject based on one or more contours of the portion. The computing system can store an association between the subject and the plurality of measures of the tumor.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

Presented herein is a pipeline, integrating deep learning and language models to improve efficiency and accuracy in radiologist's workflow, as well as generate robust structured data abstracting patient's MRIs. A universally compatible pipeline can be integrated across major clinical platforms and shared with various institutions, marking a pivotal step towards reducing radiologist human error.

The present invention relates to a cross-modal medical image registration method based on signed distance function collaborative segmentation, comprising: dividing a cross-modal biomedical image into a floating image and a fixed image; estimating the signed distance field of the fixed image; obtaining a trained segmentation model; training a Voxel-Morph registration model; repeating steps (2) to (4) until the reconstruction model, the segmentation model, and the registration model converge; and inputting the estimated signed distance field and the calculated true signed distance field of the floating image to be registered into the converged Voxel-Morph registration model to generate a registered floating image. The present invention uses the signed distance field of the cross-modal image as a potential structural feature to effectively collaborate on segmentation and registration tasks, transforming the cross-modal image registration problem into a modality-independent single-modality registration problem, greatly improving the registration performance of cross-modal biomedical images in small sample scenarios, and improving the accuracy of cross-modal brain registration.