13 results about "Medical image computing" patented technology

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.

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.

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.

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.