839 results about "Optical coherence tomography" patented technology

The invention discloses a bone joint lesion area automatic labeling method and system based on image processing, and belongs to the field of bone joint lesion area labeling. According to the invention, a bone joint reflection signal is obtained by combining the polarization-state-adjustable laser light source with polarization-sensitive optical coherence tomography, and a three-dimensional birefringence distribution diagram is generated through matrix analysis. And after non-local mean filtering is carried out on the distribution map, a lesion edge contour is extracted based on a dynamic gradient amplitude, and a binary mask is generated. Mask pixels are converted into network nodes containing space coordinates, gradient directions and birefringence, and a topological network is constructed based on the Euclidean distance of adjacent pixels. The weight is calculated through the node intersection density and the birefringence, and a graph attention network is utilized to aggregate multi-layer features to extract lesion topological features. The features are mapped to a three-dimensional space to construct a lesion probability distribution field, an interactive labeling result is generated through contour surface extraction and transparency mixed rendering, and the accuracy of labeling the complex lesion area of the bone joint can be improved.

The invention belongs to the technical field of film quality analysis and evaluation, and particularly relates to a flexible circuit board film pressing quality detection and analysis method and system. The method comprises the steps of generating a self-adaptive scanning path based on a design file and surface deformation data; performing multi-modal data acquisition and fusion along a path through optical surface scanning, ultrasonic tomography and an optical coherence tomography imaging assembly; potential defects are positioned, and size, shape, position and defect type probability values are quantized; judging the authenticity of the defect by adopting a three-stage mechanism of cross-modal probability comparison, physical characteristic logic verification and multi-frame time sequence verification; and performing weighted scoring on the real defect matching acceptance standard table, and performing disqualification marking or outputting a process optimization suggestion to the pressing equipment according to a scoring result. According to the invention, the imaging limitation of the transparent coating layer is broken through, the internal interface layering and the precise detection of the microbubbles are realized, and the closed-loop control from detection to production adjustment is formed.

The invention relates to the technical field of ophthalmologic image processing, and discloses a fundus blood vessel segmentation method based on multi-modal data. The method comprises the following steps: firstly, synchronously acquiring an optical coherence tomography image, a color fundus photographic image and a fluorescent angiography image to form multi-modal fundus data; preprocessing the eye fundus image data set to generate a standardized multi-mode eye fundus image data set; fusing heterogeneous features based on the data set, and constructing a multi-dimensional fundus feature space; using the space to train a deep learning segmentation network model, and generating an initial fundus blood vessel segmentation mask; carrying out confidence evaluation analysis on the initial mask to obtain a blood vessel segmentation result confidence distribution map; dividing blood vessel segmentation quality grades according to the distribution diagram, and defining a judgment rule; and performing interactive correction on the low-confidence region based on a rule to generate a final optimized fundus blood vessel segmentation result. And the final result is transmitted to a visual terminal for three-dimensional topology reconstruction. According to the method, the advantages of multi-modal data are integrated, and a more comprehensive fundus blood vessel segmentation result is formed.

The invention discloses a blood vessel plaque intelligent identification method and system based on optical coherence tomography, and relates to the technical field of medical image processing. Firstly, a three-dimensional blood vessel grid model is constructed through OCT volume data, vertex curvature parameters are quantified to generate a dynamic threshold template, the segmentation process is made to adapt to the local form of a blood vessel, and the problem of wrong segmentation of a traditional algorithm in a complex anatomical region is solved. Secondly, introducing a fractal dimension verification mechanism, quantitatively evaluating an initial segmentation result based on edge complexity, and when fractal features deviate from a preset threshold value, dynamically adjusting curvature weight parameters and iteratively optimizing segmentation, forming'segmentation-verification-correction 'closed-loop control, and remarkably suppressing artifact interference; and the finally output three-dimensional identification result comprises plaque space coordinates and morphological characteristics, and a visual decision basis is provided for clinic.

The invention relates to the technical field of image processing, and provides an OCT (Optical Coherence Tomography) image splicing method, which comprises the following steps: acquiring image pairs and matching point pairs corresponding to the image pairs, the image pairs being adjacent OCT images; calculating a homography matrix based on the matching point pairs so as to align the image pairs, and outputting preliminary aligned image pairs; constructing a fusion weight map, wherein the fusion weight map comprises a weight value of a pixel position and a region type; and according to the weight value and the region type, carrying out fusion splicing on the pixel positions so as to output a panoramic OCT image. According to the method, partition fusion is achieved by fusing the weight map, the problems of tissue dislocation and artifacts in OCT splicing are solved, and then the problem that the splicing effect of OCT image splicing is poor is solved.

The invention discloses a multi-stage loss optimization OCTA (Optical Coherence Tomography Angiography) image diagnosis method based on semi-supervised learning and knowledge distillation. The method comprises the following steps: (1) constructing a teacher-student model framework in combination with semi-supervised learning; (2) data set processing: randomly removing part of sample labels of the training set as label-free data; (3) training a teacher model by using the labeled data, then generating a pseudo label of non-labeled data through the teacher model, and only retaining a high-quality pseudo label meeting a confidence threshold; and (4) training a student model by using a multi-stage loss function, wherein the student model comprises supervision loss of label data, supervision loss of pseudo-label data and knowledge distillation loss. Dependence on annotation data is reduced through semi-supervised learning, the performance of the lightweight student model is improved through knowledge distillation, and the method is suitable for scenes with limited clinical resources.

The invention relates to the technical field of medical image processing and artificial intelligence, and discloses an artificial intelligence-based retinochoroid disease course structure change monitoring system, which comprises an image acquisition unit, a feature extraction unit, a lesion segmentation unit and the like. The image acquisition unit acquires retina optical coherence tomography sequence image data and fundus color image data; the feature extraction unit extracts a retina choroidal structure feature tensor through a three-dimensional convolutional neural network; the lesion segmentation unit outputs a lesion area probability distribution diagram by using a U-Net segmentation network; the time sequence alignment unit is used for registering the multi-time-point images to generate a displacement change matrix; the dynamic analysis unit extracts related indexes; an abnormal scoring unit constructs a disease course progress score; the decision grading unit outputs disease course stage classification labels; the multi-modal fusion unit fuses the multi-modal features; the report generation unit generates a structured disease course monitoring report. And favorable support is provided for diagnosis and treatment of ophthalmic diseases and illness state tracking.

The invention relates to the field of ulcerative colitis, and discloses a multi-parameter monitoring system for evaluating the intestinal barrier function of ulcerative colitis, which is used for realizing the goal of automatically adjusting a treatment scheme according to a monitoring result by constructing a closed-loop feedback treatment system. According to the invention, by designing the nanoprobe of which the surface is modified with a specific binding element and combining with a swallowing capsule endoscopy, real-time imaging and molecular level dynamic tracking of the intestinal barrier are realized, genetically engineered bacteria are constructed to quantify intestinal permeability abnormality and local inflammation level, and an intestinal barrier function damage score and a disease progress prediction result are output; individual monitoring is achieved through transfer learning, ultrasonic elastography, optical coherence tomography, wearable equipment for measuring intestinal electric field impedance spectroscopy and other technologies are adopted, an intestinal wall mechanics-electrophysiology composite parameter set is obtained, and the evaluation accuracy is improved.

The subject invention pertains to deep learning (DL) systems and methods for the binary classification of Alzheimer's Disease. Optical coherence tomography (OCT) generated reports were used, and the images were grouped into 3 different inputs: (1) an optic nerve head (ONH) model including a retinal nerve fiber layer (RNFL) thickness map, a RNFL deviation map, and an ONH-centered en face image; (2) a Macula model including a ganglion cell inner plexiform layer (GCIPL) thickness map, a GCIPL deviation map, a macular thickness map, and a macula-centered en face image; and (3) a General model including all images of (1) and (2). A fusion network is provided to analyze the plurality of images from a single eye for classification. The fusion network includes Feature Extraction, Feature Fusion, and Feature Reconstruction. Ensemble learning is provided to advantageously combine several baseline models to build a single but more powerful model.

The invention discloses a cell image segmentation method, and relates to the technical field of cell analysis, and the method comprises the following steps: S1, image acquisition; s2, processing optical artifacts generated by imaging of the phase contrast microscope; s3, processing interference of impurities and debris in the cell image; in the method, a phase contrast microscope and an optical coherence tomography microscope are combined to obtain a cell image, three-dimensional structure information of an OCT image is utilized to assist in correcting artifacts generated by imaging of the phase contrast microscope, and the artifacts are processed based on phase recovery and filtering and texture feature matching and repairing. According to the method, the interference of impurities and debris is removed by applying a diffusion and repair method based on a partial differential equation and a machine learning classification and reconstruction method, so that the cell image quality is effectively improved, the interference of artifacts and impurities on subsequent analysis is reduced, and a reliable image data basis is provided for cell image segmentation and cell morphology analysis.

The invention provides an eye fundus image intelligent enhancement method and system based on multi-modal image fusion. The method comprises the following steps of obtaining a color eye fundus photography CFP image and an optical coherence tomography OCT image and performing preprocessing; establishing an image space coordinate mapping relation for the preprocessed color fundus photography CFP image and the preprocessed optical coherence tomography OCT image, and performing feature space alignment; the first branch network structure and the second branch network structure of the multi-layer Transform feature fusion module are used for carrying out feature extraction and enhancement on the features of the processed color fundus photography CFP image and the features of the processed optical coherence tomography OCT image respectively, and the features of the color fundus photography CFP image and the features of the processed optical coherence tomography OCT image are fused; multi-modal fusion image features are obtained; according to the multi-modal fusion image features, a deep learning model is adopted to reconstruct an eye fundus image; and constructing an image enhancement model to obtain an enhanced fundus image.

The invention discloses an optical coherence tomography device for optimizing aberration digital compensation and an imaging method, the device comprises an optical coherence tomography device and an image processing module, and the optical coherence tomography device comprises an illumination module, an interference module, an area array detection module and a synchronous control module. The imaging method comprises the following steps: setting device light source parameters according to coherent gating, and determining a sampling matching relation of the area array detection module according to a sampling theorem; selecting a frequency sweeping mode or a time domain mode, electrifying to activate a corresponding light source and a synchronous time sequence, and adjusting the position of a reference mirror; collecting a full-field interference sequence of the area-array camera, performing digital aberration compensation, performing compensation and denoising on volume data of different depths, and exporting a three-dimensional diffraction limit image; and outputting a three-dimensional tomographic image with transverse diffraction limit and axial micron-level resolution. According to the invention, the imaging resolution and the imaging speed of optical coherence tomography are improved under the conditions that the camera specification is reduced, the GPU dependence is reduced and the system cost is reduced.

The invention discloses a parallel imaging OCT (Optical Coherence Tomography) system based on a metasurface area array, which relates to the technical field of medical instruments, is small in size and can realize intraocular single-shot area array imaging and a three-dimensional position depth perception function by using metasurface and optical coherence tomography imaging information in a sample tissue with a limited size. According to the technical scheme, a light beam emitted by a laser is divided into two paths through a beam splitter, and the two paths enter a sample arm module and a reference arm module respectively; the sample arm module is a probe and comprises a multi-core optical fiber and a first metasurface; the reference arm module comprises a collimator, a motor and a right-angle reflector; the probe enters a sample tissue, a light beam is focused on the sample tissue and then is backscattered, the backscattered light beam reversely passes through the first metasurface, the multi-core optical fiber, the beam splitter, the second metasurface, the third lens, the grating and the focusing lens and then forms a spectral line beam on the CCD sensor, and the computer reads a signal from the CCD sensor and performs three-dimensional reconstruction imaging on the sample tissue.

A tunable laser assembly housed in a single enclosure and a method of control is described wherein the tunable laser, pump and semiconductor optical amplifier do not share a common optical axis but are all aligned to optical waveguides on an intervening planar lightwave circuit (PLC). Wavelength monitoring circuitry is included on the PLC to enable monitoring and control of the tunable laser center wavelength and optical bandwidth. The design of the PLC does not introduce perturbations into the swept-source laser output spectrum that would cause artifacts in imaging applications such as optical coherence tomography (OCT).

The invention discloses an optical coherence tomography optical path structure and a dispersion compensation method thereof. The light path structure comprises a coherent light source, a 2 * 2 optical fiber coupler, a reference light path, a sample light path, an equal-wave-number spectrograph and an upper computer; low-coherence broadband light emitted by the coherent light source is divided into reference light and sample light after passing through the 2 * 2 optical fiber coupler, the reference light and the sample light are sequentially transmitted in a reference light path and a sample light path, the reference light and the sample light are retroreflected through a sample and a reference mirror and are retroreflected to the 2 * 2 optical fiber coupler according to an original light path, interference is generated, and interference light beams are transmitted to the equal-wave-number spectrometer. Interference light beams are split to an upper computer according to equal wave number intervals, and a tomographic image of a sample is obtained through algorithm processing of the upper computer. By means of the structure, the existing dispersion loss problem is solved from the source, most dispersion generated by a light path is solved, other dispersion is eliminated by adding a dispersion compensating mirror made of optical glass materials with certain thickness in the light path through algorithm calculation, the algorithm is simple, convenient and fast, and the imaging speed is high.

The invention relates to the technical field of laser cleaning processing, and particularly discloses a real-time monitoring method for laser cleaning of a carbon fiber composite material. An optical coherence tomography (OCT) detection unit and a sensor module are innovatively adopted to form a real-time monitoring system, and according to the surface treatment requirement of the carbon fiber composite material, a temperature sensor in the sensor module is used for collecting a heat radiation signal of a laser irradiation area in real time, and the surface layer temperature of a base material during machining is monitored in real time; the distance between a machining head and a base material is dynamically tracked through a laser ranging module in the sensor module, the OCT detection unit obtains micron-sized surface topography characteristics through frequency domain interference signal analysis, and surface roughness parameters are synchronously calculated. The data of the three channels are fused through the edge calculation module and then fed back in real time through the displayer, and the temperature field, the roughness and the focal length are effectively monitored in the cleaning process. According to the method, damage to the base material due to factors such as laser ablation can be avoided, the operation time is effectively shortened, and the operation efficiency is improved.

Surgical marker systems and methods for delineating a lesion margin of a subject are provided. An example system includes a handheld probe device configured to capture an optical coherence tomography (OCT) image and a processor coupled to a memory. The handheld probe device includes a handheld probe including a fiber-optic probe assembly and a marker assembly. The processor is configured to: segment, by a neural network, each pixels of the OCT into different tissue-type categories; generate one or more feature vectors based at least in part on the segmented pixels; determine, by a one-class classifier, a boundary location in the OCT image between a normal tissue and an abnormal tissue of the tissue structure; and control the marker assembly to selectively create a visible label on a tissue location of the subject, the tissue location corresponding to the boundary location.

The invention discloses a bimodal optical coherence elastography device and method, and the device comprises an excitation unit which is used for generating a sound field; the imaging unit is an optical coherence tomography system and is used for detecting vibration of the sample to be detected, namely strain response and elastic wave response; and the coupling unit is provided with a sound reflecting surface with a middle through hole and is used for reflecting the sound field generated by the excitation unit, so that the detection light beam emitted by the imaging unit and the sound field emitted by the excitation unit are coupled and irradiated on the same side of the to-be-detected sample. According to the invention, the multi-beam optical coherence tomography system is utilized to emit two or more detection beams to carry out bimodal elastic imaging, high-resolution strain information and quantitative elastic information of the sample to be detected can be obtained at the same time, and the problem that strain imaging and elastic wave imaging cannot be carried out at the same time in the prior art is solved.

The invention relates to the technical field of femtosecond laser, and discloses a multifunctional ophthalmology femtosecond device and a use method, and the device comprises a scanning imaging module which is used for emitting a detection light beam and carrying out OCT imaging; the femtosecond laser scanning module is used for receiving the detection light beam and irradiating the detection light beam to the target tissue; the femtosecond laser scanning module is also used for generating femtosecond laser and focusing the femtosecond laser to the target tissue; the operating microscope module is used for receiving the detection light beam and irradiating the detection light beam to a target tissue; the operating microscope module is also used for carrying out microscope imaging. According to the method, the optical coherence tomography technology is fused, more imaging information is added on the basis of an original visible light image, the re-alignment precision can be effectively improved, and the problem that a separation hook enters a wrong corneal lens layer is avoided.

The invention discloses an optical coherence tomography detection method for aero-engine thermal growth oxides, and relates to the field of optical detection. The method comprises the following steps: acquiring interference signals of a reference light field and a sample scattering light field, and performing adaptive polarization state compensation on the interference signals based on a dynamic polarization matching factor to obtain compensated interference signals; anisotropic filtering is carried out on the compensation interference signal, oxide layer columnar crystal structure features are separated, and a filtering signal is obtained; performing time-frequency joint analysis on the filtering signal, optimizing a time-frequency transformation kernel parameter by using an adaptive window width adjustment factor, and extracting oxide layer thickness and frequency band energy distribution; and fusing the columnar crystal structure characteristics and the frequency band energy distribution, constructing a cross-scale thermodynamic inversion model, and outputting a prediction result of the residual life of the thermal growth oxide layer of the engine. By introducing polarization compensation, frequency domain analysis optimization and a multi-field coupling inversion model, the imaging definition, the thickness extraction precision and the residual life prediction capability of the thermal growth oxide layer are remarkably improved.

Blood flow information is extracted from speckle information of a time series of structural optical coherence tomography (OCT) images. Flow information can be determined from high-frequency information of the OCT images, a speckle density of the OCT images, from a co-occurrence matrix applied to the OCT image, from a machine learning analysis of an input OCT image, or the like. A flow profile analogous to a pulse waveform is then generated as a time series of the extracted flow information.

The invention provides an intraoperative real-time navigation system based on digital twinning, and relates to the technical field of digital twinning, and the system comprises a data collection module which is used for obtaining the three-dimensional form data of a liver and a biliary tract through an intraoperative ultrasonic imaging device, and collecting the elastic modulus distribution parameters of liver parenchyma and a bile duct wall; the construction registration module is used for constructing a real-time digital twin in the liver and gall area based on the three-dimensional form data and the elastic modulus distribution parameters, and carrying out multi-mode non-rigid registration on the digital twin and the liver surface topography and the vascular structure which are subjected to optical coherence tomography in the operation; therefore, a dynamic space mapping relation between the organ structure and the image data is established. According to the method, real-time navigation in the operation is realized through digital twinning, the obstacle avoidance path is updated, and the safety and efficiency in the operation are improved.

The invention discloses a 3D printing self-adaptive control system for bone defect, and relates to the technical field of 3D printing self-adaptive control, a miniaturized optical coherence tomography, a high-resolution camera, a force sense and temperature sensor and the like are installed on an operation site, and the whole process of material deposition and solidification is monitored; then, deviation is rapidly recognized through an anomaly detection and error prediction model, defect positions are output, and a closed-loop control unit is combined with model prediction control and self-adaptive path planning to achieve online adjustment of key variables such as the extrusion amount and the printing speed; when the defect cannot be made up automatically through parameter adjustment, the system can trigger local shutdown and secondary repair, the forming quality is evaluated through multi-modal scanning and a repair scoring function after repair, and the high matching degree and mechanical stability of the final bone scaffold and the bone defect part are ensured; the cooperation of multi-modal sensing and local path planning enables the system to have higher adaptability.

An optical coherence tomography (OCT) system comprises an interferometer configured to split incident light into a reference beam and a test beam, and to interfere the test beam reflected from the specimen with the reference beam reflected from a reference mirror to produce an interference pattern. The OCT system also comprises a spectrometer configured to analyze spectral components of the interference pattern at non-uniformly sampled wavenumbers. A computer-readable memory of the OCT system is configured to store a measurement model with elements connecting different depth values with different non-uniformly sampled wavenumbers and weighted with weights derived from a power spectral density (PSD) of the incident light for corresponding wavenumbers. The OCT system further comprises a processor configured to determine the profilometry measurements of the specimen as a maximum likelihood estimate of the specimen surface depth by back-projection of the measured intensities with the measurement model.

The invention relates to an adaptive weighted bilateral filtering deconvolution OCT image processing method and system, and the method comprises the steps: carrying out the preprocessing of an original OCT signal, including background noise removal, wave number calibration, spectrum shaping and dispersion compensation, so as to obtain an OCT reconstructed image; enhancing the contrast ratio through histogram stretching, adjusting the space weight and the intensity weight of the OCT stretching image, and performing adaptive denoising on the image; and further optimizing the image by using a blind deconvolution algorithm, and iteratively updating the estimated image and a point spread function (PSF) to obtain a high-definition OCT reconstructed image. According to the method, efficient denoising and detail reservation of the OCT image are realized through adaptive weighted bilateral filtering and blind deconvolution technologies, so that a high-definition reconstructed image is obtained.

Disclosed is a method (100) for correcting artifacts in Optical Coherence Tomography (OCT) images. The method involves acquiring an OCT image and subsequently transforming it from a Cartesian coordinate system to a polar coordinate system via polar reconstruction, generating a reconstructed polar OCT image. This image is then segmented into RED, GREEN, BLUE color channels. Fourier transformation is applied to each channel, transitioning them into their frequency domain representations. A custom frequency mask is utilized on these images, filtering out artifact-related frequencies. Following this, an inverse Fourier transformation is executed, reverting the images back to their Cartesian format. These images are then combined to produce a reconstituted OCT image. Further, this reconstituted image is merged with the original OCT image, resulting in an OCT image that is substantially free from artifacts.

The invention relates to an OCT (Optical Coherence Tomography) navigation uterine cavity adhesion and dissection postoperative residual adhesion prediction method, which comprises the following steps of: acquiring an interference signal of an intraoperative uterine cavity wall area by utilizing OCT equipment, and identifying an adhesion fiber microstructure which is not completely broken so as to judge the existence of an optically invisible residual adhesion microbridge; applying low-frequency bioelectrical stimulation to the wounded area of the uterine cavity during the operation, and marking the wounded area as a tissue repair electrical activity silent area if no blood flow micro-reaction is continuously detected; oCT micro-deformation sequence analysis is carried out on the region after intraoperative dissection is completed, mutation breakpoints of tissue elastic distribution are identified, and the potential risk that traction after-tack occurs in the region is predicted; detecting whether a metabolic oxidation reaction residual zone exists in the operation area or not so as to judge local tissue repair activity decline and re-adhesion tendency; local micro-intervention stimulation is applied to the tissue area judged to be high in risk, OCT imaging is carried out again to detect the reversibility of tissue optical response, and if an image is still fractured and red shift is enhanced, it is judged that the area is the postoperative irreversible adhesion residual area.

Systems and methods for compensating for tilt of an intraocular lens (IOL) and systems and methods for adjusting an IOL are disclosed herein. For example, one of the methods may include capturing one or more optical coherence tomography (OCT) images of a subject's eye while implanting the IOL within the subject's eye. The method may further include generating a fixation target such that the fixation target is visible to an eye of the subject; and moving the fixation target until the lateral plane of the IOL is perpendicular or substantially perpendicular to the optical axis of the ophthalmic system. The method may also include directing a laser beam generated by a laser of the ophthalmic system at the IOL.

The invention relates to the technical field of oral medicine, in particular to an intelligent navigation system and method for alveolar bone finishing based on image data fusion, and the system comprises a real-time bone mineral density sensing module which obtains a bone mineral density value through laser scanning and adjusts the temperature; the personalized bone mineral density self-adaptive control module is used for adjusting a bone trimming tool and temperature compensation according to the bone mineral density value; the multi-sensor data fusion module is used for dynamically monitoring the bleeding amount and compensating mechanical vibration of equipment; and the bone cutting path space sensing module is used for acquiring a bone cutting path by combining laser scanning data, CT data and optical coherence tomography data, and determining a real-time path of the tool according to temperature and bleeding amount change. According to the system, through an anti-penetration protection mechanism combining real-time bone mineral density perception, multi-scale tactile feedback and visual enhancement, the penetration risk is remarkably reduced, the operation safety is improved, and the penetration risk is reduced by 93% through the anti-penetration mechanism.

The invention discloses a glass-based circuit board defect detection method and system, and relates to the technical field of glass substrate defect detection.The method comprises the steps that an optical coherence tomography system with the fixed scanning resolution of 320 * 320 is used for collecting a tomographic image of a target area, three-dimensional reconstruction is conducted on the tomographic image, and a three-dimensional body data model is obtained; extracting a plurality of three-dimensional morphological characteristics such as defect depth, transverse distribution profile and defect internal refractive index unevenness from the model; and inputting the features into a defect classifier constructed based on a three-dimensional convolutional neural network for identification, and performing spatial position verification in combination with a known design layout to generate a final detection report. According to the method, the standardization of the detection process is realized through the fixed scanning resolution, and the efficiency and the result consistency are improved; by quantifying the refractive index non-uniformity in the defect, the optical property variation type defect in the material is effectively identified, and the detection depth and accuracy are enhanced.