22 results about "Medical image computing" patented technology

The invention relates to an endoscope image reflection restoration method based on detection network and filtering fusion, and belongs to the technical field of medical image computer processing. The method aims at improving detection and removal of specular reflection in an endoscope image, a variant of TransUnet is adopted in the detection stage, and U-Net and Transform architectures are combined; the method comprises Atrous spatial pyramid pooling (ASPP), information bottleneck (IB) and a convolutional block attention module (CBAM). The enhancement functions improve the identifiability of specular highlight and solve the problem of weak generalization in endoscope data concentration. In the removal stage, the invention designs an enhanced median filtering module for removing specular reflection. The module first applies Gaussian blur to the repaired region and then calculates color information from the non-reflective region. According to the method, mirror reflection is effectively eliminated from the endoscope image, clearer and more accurate visual information is provided for clinicians, and improvement of diagnosis precision is facilitated.

The invention discloses a three-dimensional medical image visual language model pre-training method for reconstructing a hybrid strategy, which belongs to the technical field of medical image calculation and comprises the steps of constructing a medical image text pair data set, a language text mask reconstruction strategy, a visual image mask reconstruction strategy, a semantic perception fusion strategy and multi-task joint learning. According to the method, the large language model is finely adjusted, the diagnosis and attribute information in the medical report is extracted by using the finely-adjusted large language model, the efficient prompt is generated, and the large language model has relatively high generalization ability, so that the cost of manual annotation is greatly saved; according to the semantic perception fusion strategy, the text feature obtained by the text encoder and the image feature obtained by the image encoder are combined to obtain the new text feature, so that the text perceives the diagnosis and attribute information of the image in advance, the alignment of the image and the text in the embedding space is further optimized, and the pre-training efficiency is improved.

The invention relates to a colorectal lesion typing method based on a KAN network and a variable attention mechanism, and belongs to the technical field of medical image computer processing. According to the method, colorectal lesions are classified; a KAN-ResNet backbone network typing model is established and initialized; setting an optimizer and a loss function; model training; storing and outputting the model; and parting the model. According to the method, a ResNet backbone network and a KAN mechanism are combined to capture complex modes and interdependence relations in colorectal images. The KAN-ResNet model aims at improving the classification performance of a fine-grained data set, so that the accuracy of distinguishing subtle changes of colorectal lesions is improved. Layered features are effectively captured by using a deep residual learning framework of a residual network ResNet, a variable attention mechanism and complexity of adapting to lesions are combined, so that the model can focus on related areas, accurate features can still be captured although deformation exists, and adaptability and accuracy in the aspect of colorectal lesion classification can be improved.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

A method comprises receiving a user input to select a first point and a second point from a medical image, extracting a plurality of blood vessel regions from a plurality of frame images of the medical image, determining a plurality of first regions associated with the first point and a plurality of second regions associated with the second point, determining a first frame image and a second frame image with the contrast agent arriving at the first point and the second point, based on a change in pixel intensity for each of the plurality of first regions and the plurality of second regions, and computing the blood flow velocity from the first point to the second point based on a time interval between the first frame image and the second frame image and on a distance between the first point and the second point.

The invention provides an effective mucous membrane coverage rate-based colonoscope retreating quality real-time evaluation method and system, and relates to the technical field of medical image calculation and processing. The colonoscope retreating quality real-time evaluation method based on the effective mucous membrane coverage rate comprises the steps that a retreating video is obtained, and the retreating video comprises a real-time retreating video and a historical video; preprocessing the mirror retreating video to obtain an optimized data set; extracting effective frames based on the optimized data set; and inputting the effective frame into a preset image segmentation model to obtain an effective colonic mucosa coverage rate which is used for evaluating the despeculation quality. According to the colonoscope retreating quality real-time evaluation method based on the effective mucous membrane coverage rate, the technical problem that the clinical colonoscope retreating process lacks real-time supervision and feedback in the prior art is solved.

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 present application relates to the technical field of medical image computing and artificial intelligence, in particular to a sugar metabolism cross-modal image generation method based on a WARPNet framework, based on the WARPNet framework, through three core steps of multi-scale feature coding, weighted attention cross-modal reasoning and context perception generation, precise synthesis from MRI to PET is realized. The innovation lies in introducing a learnable sugar metabolism semantic prior weight in cross-modal reasoning, modulating feature attention, thereby explicitly guiding the model to focus on key metabolic areas, effectively solving the semantic gap problem between anatomical and functional modalities. The method improves the quality, physiological reasonableness and cross-dataset generalization ability of the synthesized image, and has application potential in auxiliary diagnosis, treatment planning and medical research.

The invention relates to the technical field of medical image calculation, in particular to a heart three-dimensional polygon mesh construction method and system, and the method comprises the steps: firstly obtaining and registering an image, generating an inductor density and a signed distance field based on boundary features, and fitting anatomic anchor points; determining a patient specific three-dimensional domain through mass conservation transmission and homeomorphic deformation; constructing pull-back measurement in the domain, solving three groups of harmonic functions, extracting contour surfaces according to common area constraint, and intersecting to form a network; deformation and harmonic functions are taken as joint unknown quantities, synchronous optimization is carried out in combination with pixel consistency, physical consistency and topological consistency, and the density of the inducer is re-weighted by layer area deviation feedback until convergence. According to the method, the surface net with consistent anatomy, controlled topology and balanced layer is output, and the method is adaptive to diagnosis, treatment and simulation application.

The system comprises a workstation including a controller. The controller is configured to cause the workstation to: generate a vessels-image based on at least one image from a set of medical images of a region of interest; calculate a vessel path from a source point to a target point on the vessels-image, based at least on the set of medical images; determine that at least one child vessel is connected to the vessel path based on at least a flow of contrast agent in the set of medical images; generate a path mask for the vessel path; generate a child vessel mask for the at least one child vessel; and display the path mask and the child vessel mask on an image associated with the path mask.

A medical image processing device includes a display device; and an electronic processor programmed to perform a medical image processing method including receiving a three dimensional (3D) medical image; computing least one 3D derivative image of the 3D medical image; identifying a set of fibers in the 3D medical image by tracing fibrous image features in the at least one 3D derivative image starting from respective seed locations in the least one 3D derivative image; and controlling the display device to display an anatomical representation comprising or derived from the set of fibers.

This invention discloses a 3D medical image segmentation method and system based on prior knowledge from a large language model, belonging to the field of 3D medical image computing technology. The method first constructs a medical image-text pair dataset containing 3D medical images and prior information on manually labeled organs and lesions. Then, it generates text prior knowledge by combining the prior information with a large medical language model. Text prior features and high-dimensional image features are extracted by text and image encoders, respectively. Subsequently, a multi-level semantic perception fusion strategy based on a cross-modal attention mechanism is used to fuse the dual features, generating fused semantic features. The high-dimensional image features are decoded into 3D image features matching the spatial dimensions of the original image. Finally, element-wise multiplication fusion processing is used to generate a lesion prediction mask, completing the segmentation. This invention effectively integrates prior knowledge from the medical field, solving the problems of low accuracy and poor generalization of purely data-driven segmentation methods in scenarios with small samples and small lesions.

A medical image processing device includes a display device; and an electronic processor programmed to perform a medical image processing method including receiving a three dimensional (3D) medical image; computing least one 3D derivative image of the 3D medical image; identifying a set of fibers in the 3D medical image by tracing fibrous image features in the at least one 3D derivative image starting from respective seed locations in the least one 3D derivative image; and controlling the display device to display an anatomical representation comprising or derived from the set of fibers.

A medical image processing device includes a display device; and an electronic processor programmed to perform a medical image processing method including receiving a three dimensional (3D) medical image; computing least one 3D derivative image of the 3D medical image; identifying a set of fibers in the 3D medical image by tracing fibrous image features in the at least one 3D derivative image starting from respective seed locations in the least one 3D derivative image; and controlling the display device to display an anatomical representation comprising or derived from the set of fibers.

The embodiment of the invention provides an image processing method, a model training method and a medical image computer-aided diagnosis method, and relates to the technical field of artificial intelligence, the image processing method comprises the following steps: in an image processing model, determining pruning parameters according to a text feature sequence and an image feature sequence, the image feature sequence is obtained by performing feature extraction on a to-be-processed image, and the text feature sequence is obtained by performing feature extraction on an image processing instruction of the to-be-processed image; pruning the image feature sequence according to the pruning parameters to obtain a plurality of reserved image features; and obtaining a processing result for responding to the image processing instruction based on the plurality of reserved image features and the text feature sequence. According to the technical scheme of the embodiment of the invention, adaptive pruning can be realized, and the accuracy of a model processing result is improved.

A medical image processing apparatus includes a display apparatus and an electronic processor programmed to perform a medical image processing method, the medical image processing method including: receiving a three-dimensional (3D) medical image; calculating at least one 3D derivative image of the 3D medical image; identifying a set of fibers in the 3D medical image by tracking fibrous image features in the at least one 3D derivative image starting from respective seed locations in the at least one 3D derivative image; and controlling the display device to display an anatomical representation comprising or derived from the set of fibers.

A medical image processing device includes a display device; and an electronic processor programmed to perform a medical image processing method including receiving a three dimensional (3D) medical image; computing least one 3D derivative image of the 3D medical image; identifying a set of fibers in the 3D medical image by tracing fibrous image features in the at least one 3D derivative image starting from respective seed locations in the least one 3D derivative image; and controlling the display device to display an anatomical representation comprising or derived from the set of fibers.

This invention belongs to the field of computer-aided medical image technology, specifically relating to an improved self-supervised detection method for mammogram images. Based on Faster R-CNN and combined with improved self-supervised pre-training, this invention can effectively detect lesion regions in mammogram images and classify them as benign or malignant. Using a computer-aided diagnostic system to assist doctors in diagnosis significantly reduces their workload, which is of great significance. Compared to natural images, medical image data is difficult to obtain and annotation is expensive. Therefore, the object detection in this invention combines self-supervised pre-training, allowing the model to achieve better results using unlabeled images.

A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain data related to circulating tumor deoxyribonucleic acid (ctDNA) of a subject. The processing circuitry is configured to specify a gene on the basis of information about a false negative in a result of estimating a genetic mutation in a tumor tissue of the subject based on the data related to the ctDNA. With respect to the gene specified on the basis of the information about the false negative, the processing circuitry is configured to calculate a result of estimating the genetic mutation in the tumor tissue, on the basis of a medical image of the tumor tissue of the subject.

A medical image display apparatus comprising a hardware processor that acquires a series of medical images including a plurality of medical images, performs image analysis on the plurality of medical images to extract a plurality of medical images each including a lesion candidate region, for at least two medical images among the extracted plurality of medical images each including the lesion candidate region, calculates lesion information on the lesion candidate region included in each of the at least two medical images, and based on the lesion information on each of the at least two medical images from each of which the lesion information has been calculated, controls display of information indicating presence of the lesion candidate region about each of the plurality of medical images each including the lesion candidate region.

The invention relates to the technical field of medical images, and discloses a spinal nerve root compression condition assessment method and device, and electronic equipment, and the compression condition assessment method comprises the steps: recognizing and marking a spine, an intervertebral disc, a lateral recess and a spinal nerve root in an original medical image of a patient, so as to obtain a target medical image; calculating feature parameters of a spine, an intervertebral disc, a lateral recess and a spinal nerve root based on the target medical image; based on the characteristic parameters of the spine, the intervertebral disc and the lateral crypt, whether the spinal nerve root has the risk of compression or not is determined; and under the condition that the spinal nerve root has the compression risk, determining the compression degree of the spinal nerve root based on the characteristic parameters of the spinal nerve root. According to the method, detailed anatomical structure information and quantitative analysis results can be provided, the diagnosis accuracy is remarkably improved, the risks of misdiagnosis and missed diagnosis are further reduced through multi-dimensional risk assessment and compression degree grading, and the compression condition of the spinal nerve root is accurately and efficiently assessed.