12 results about "Microcalcification" patented technology

The present disclosure provides systems and methods for Al model(s) that are trained to identify characteristics associated with lesions within a blood vessel and provide a suggested procedure associated with the lesion. The Al model may receive, as input, intravascular and / or extraluminal image data associated with the blood vessel and provide, as output, characteristics, e.g., characterizations, associated with the blood vessel. The characterizations may include, for example, form or tissue nature (e.g., microcalcification, punctate, fragment, sheet, nodular, or otherwise), location within the vessel (e.g., intimal, superficial, medial, deep, etc.), size (e.g., length, depth, thickness, etc.), severity, and / or the like. The characterizations may be used as input to the same and / or a different model for providing a suggested procedure associated with the lesion. The suggested procedure may include, for example, orbital atherectomy, intravascular lithotripsy, rotational atherectomy, cutting and / or scoring balloons, and / or the like.

The present application relates to the technical field of calcification point segmentation, and particularly relates to a breast molybdenum target image micro-calcification point identification and detection method based on deep learning. The present application performs image segmentation on a breast molybdenum target image based on each segmentation threshold, and determines a suspected micro-calcification point region under each segmentation threshold. Based on the morphological features, light and dark features and texture features of the suspected micro-calcification point region, a feature vector is determined, and a trained neural network is used to determine the confidence score of the suspected micro-calcification point region. The confidence scores of each pixel point in the suspected micro-calcification point region under different segmentation thresholds are corrected and fused to determine a final score and determine a final micro-calcification point region. Through the cooperative verification and fusion of multiple thresholds, the present application eliminates local tissue interference when the global threshold is determined, and improves the detection sensitivity of micro-calcification point identification and the accuracy of boundary extraction.

This application provides a medical image analysis method, electronic device, and computer program product. Through the design of a dual-frequency uncertainty network model, it achieves precise capture and uncertainty quantification of high-frequency and low-frequency pathological features at the architectural level. It not only outputs the final prediction result but also simultaneously generates uncertainty scores, ensuring that model predictions are not limited to a single probability value but consider both diagnostic results and risk confidence levels, making the assessment results highly consistent with the actual difficulty of clinical diagnosis. The dual-frequency uncertainty network model, through the collaborative design of an encoder, a dual-frequency masking processing module, and a decision module, constructs an analysis framework that fits the patterns of clinical pathological features. The dual-frequency masking processing module is specifically designed for the high-frequency and low-frequency features of breast images, accurately capturing microscopic edge features (high-frequency components such as spiculations, microcalcifications, and angularity) and macroscopic morphological structures (low-frequency components such as aspect ratio and growth direction), achieving full-dimensional coverage of pathological features.

A method of imaging a breast microcalcification feature by acquiring a tomographic imaging data set and a stationary imaging data set and combining the tomographic imaging data set with the stationary imaging data set to improve resolution and reduce blur in the resulting combined image. The resulting improve resolution image data set is evaluated for identification of breast calcifications for purposes of breast cancer screening, diagnosis and / or treatment.

The present disclosure provides systems and methods for AI models that are trained layers to identify characteristics associated with a lesion in a blood vessel and provide suggested procedures associated with the lesion. The AI model may receive as input intravascular and / or extraluminal image data associated with the blood vessel and provide as output characteristics associated with the blood vessel, such as characterization. The characterization may include, for example, form or tissue properties (e.g., microcalcification, punctiform, fragment-like, sheet-like, nodule-like, or others), location within the vessel (e.g., inner layer, superficial layer, middle layer, depth, etc.), size (e.g., length, depth, thickness, etc.), severity, and / or the like. The characterization may be used as input to the same and / or different models to provide a suggested procedure associated with a lesion. The proposed procedures may include, for example, orbital atherectomy, endovascular lithotripsy, rotary atherectomy, cutting and / or scoring balloons, and / or the like.

BREAST CANCER DETECTION MACHINE USING MICRO CALCIFICATIONS

A medical imaging method is disclosed herein. The method includes receiving (202) gradient echo magnetic resonance imaging data (123) describing a breast region (319) of a subject (318). The method also includes receiving (202) DIXON magnetic resonance imaging data describing the breast region. The DIXON magnetic resonance imaging data and the gradient echo magnetic resonance imaging data are spatially matched. The method also includes generating (204) a breast microcalcification magnetic resonance image (128) by inputting the gradient echo magnetic resonance imaging data and the DIXON magnetic resonance imaging data to a breast microcalcification image reconstruction module (122). The breast microcalcification image reconstruction module is configured to output a breast microcalcification magnetic resonance image in response to inputting the gradient echo magnetic resonance imaging data and the DIXON magnetic resonance imaging data. The breast microcalcification magnetic resonance image describes a location of breast microcalcification, wherein the gradient echo magnetic resonance imaging data is a phase image.

The invention discloses a mammary gland micro-calcification ultrasonic super-resolution imaging device which comprises a bed body, a side frame is fixedly installed on one side of the bed body, and a detection device body used for examining the mammary gland is arranged on one side of the bed body. Forward extrusion elastic force generated when the auxiliary spring is compressed and nonlinear negative repulsive force generated when the fixed magnetic ring and the lifting magnetic ring with the same magnetic poles are close to each other are subjected to mechanical parallel superposition, and in a set patient breathing fluctuation displacement window, downward elastic force increased by the spring is accurately counteracted by upward repulsive force increased by the magnetic rings; the whole rigidity of the system is close to zero, the self-adaptive follow-up effect that displacement changes and output pressure is absolutely constant is achieved, the design thoroughly avoids the pain point of artificial extrusion of a false blood flow-free blind area due to the fact that superficial microcirculation of the mammary gland is instantaneously cut off by overlarge probe pressing force, and the natural flow state of a microbubble contrast agent in a complex flow channel network is guaranteed.

The invention relates to a thyroid cancer side neck lymph node metastasis prediction model construction method and application. The thyroid cancer side neck lymph node metastasis prediction model comprises a Logistic regression model and a decision process module. The Logistic regression model is used for calculating an actual risk value based on a value of a primary tumor maximum diameter, a value of a tumor boundary, a value of a BRAF V600E mutation state, a value of micro-calcification existence, a value of an aspect ratio and a value of single / double-side cancerous lesion; the decision process module is used for giving a decision suggestion according to the calculated actual risk value and a set decision process; the model provides a new quantitative tool for preoperative evaluation of papillary thyroid carcinoma patients, can provide individualized and precise treatment schemes for clinic, reduces unnecessary treatment and missed diagnosis, and finally improves the treatment effect and life quality of the patients.

This application provides a computer-aided detection and diagnosis system for breast microcalcifications, comprising: an acquisition unit for acquiring at least two breast X-ray images of the same breast tissue from different projection angles as a target view group; a region detection unit for detecting suspected microcalcification candidate regions in each single view to obtain a candidate region set corresponding to each single view; based on the geometric projection relationship between different views in the target view group, performing cross-view pairing and association on candidate regions from different views to establish candidate region pairs characterizing the image appearance of the same potential microcalcification at different projection angles; an extraction module for extracting cross-view consistency features for each candidate region pair; a calculation unit for fusing the cross-view consistency features corresponding to each candidate region pair and its internal image features in each single view to form an enhanced feature vector of the potential microcalcification; and obtaining the benign or malignant classification result of the corresponding potential microcalcification.

This invention relates to a deep learning-based method for breast calcification analysis. By enhancing image representations, the method improves the model's ability to identify blurred and microcalcifications. An improved image segmentation network, UNeXt, based on a convolutional multilayer perceptron, effectively segments breast calcifications from images, significantly reducing computational overhead and costs. Combining deep learning with breast calcification features for analysis assists doctors in image interpretation and reduces errors. For the breast calcification analysis model, Focal loss + Dice loss are used as initial training functions, followed by HD loss for further training, thereby improving the model's learning ability. The deep learning-based breast calcification analysis method disclosed in this invention can serve as an auxiliary screening tool for early breast cancer, assisting doctors in better identifying and defining the morphology, size, and distribution of breast calcifications within the medical system.

The present invention relates to an assessment device and tool for medical professionals to increase the success rates of the cancer removal surgeries of breast cancer patients. The removed breast tissue specimen is brought to an X-ray imaging device (200). A first image is taken from the whole specimen (203), and interesting areas (204a) are found either by machine-assisted tools or by human assessment on the actual image. Then a geometric magnification may be performed on the interesting area(s) (204a). The device may provide pixel-specific graphs on the intensities, and this gives help in finding microcalcifications in the specimen (203), comprising calcium oxalate (301) and calcium phosphate (302). The microcalcifications act as markers for various types of tumours. The closest distance from the found carcinoma (304) to the surface of the specimen (203) is also assessed as the healthy margin (303). The result is a human- & machine-made combined assessment, whether the surgery needs to be continued or not.