A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

A fluorescent imaging device includes a light source unit including a first light source for radiating excitation light, a second light source for radiating visible illumination light, and a third light source for radiating non-visible light, an imager being configured to capture a fluorescent image, a visible image, and a non-visible image, and a tracking processor that is operable to perform moving-body tracking for a region of interest that is set in an image based on at least the non-visible image.

A super-resolution reconstruction preprocessing method of contrast-enhanced ultrasound images includes: acquiring an image set to be preprocessed; acquiring grayscale fluctuation signal of a pixel point in the registered contrast-enhanced ultrasound images to be preprocessed; performing denoise reconstruction operation on the image set to be preprocessed to obtain a reconstructed feature parameter image, and performing interpolation calculation on the reconstructed feature parameter image to obtain a sparse microbubble image. By analyzing the grayscale fluctuation signals of the collocated pixel point set in the plurality of frames of the registered contrast-enhanced ultrasound images to be preprocessed, a signal-to-noise ratio and a signal-to-background ratio are improved. By performing interpolation operation on the reconstructed feature parameter image, spatial decoupling of overlapping microbubbles is realized, and influence of strong noise and high concentration microbubble on the accuracy of super-resolution reconstruction is reduced.

Methods and apparatus for predicting performance of an individual on a task, the method comprises receiving brain imaging data for the individual, wherein the brain imaging data comprises structural brain data, determining values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels, and predicting based on the determined values, a performance potential of the individual.

Methods and apparatus for determining a functional impact of vascular lesions are disclosed. An example method includes calculating estimates of a single functional blood flow metric (for example, fractional flow reserve calculated from angiographic images) for multiple locations in each of a plurality of connected vascular branches. The method includes converting these estimates into FFR impact scores, which are indicative of the overall impact of occlusive vascular disease on the connected vascular branches.

Provided are a diagnostic system for predicting fractional flow reserve (FFR) through a machine learning algorithm based on an ultrasound image of a coronary artery and diagnosing the presence of a coronary artery lesion, and a diagnostic method thereof. The diagnostic method of diagnosing an ischemic lesion of a coronary artery includes: obtaining an intravascular ultrasound (IVUS) image of a coronary artery lesion of a patient; obtaining a mask image in which a vascular lumen is separated, by inputting the IVUS image into a first artificial intelligence model; extracting an IVUS feature from the mask image; and obtaining an FFR prediction value by inputting information including the IVUS feature into a second artificial intelligence model, and determining presence of an ischemic lesion.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

A method for extracting a major vessel region from a vessel image by a processor may comprise the steps of: extracting an entire vessel region from a vessel image; extracting a major vessel region from the vessel image on the basis of a machine learning model which extracts a major vessel region; and revising the major vessel region by connecting separated vessel portions on the basis of the entire vessel region.

In one embodiment, a system for biomedical object segmentation includes one or more processors; one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored on the one or more memory modules. The machine readable instructions cause the system to perform the following when executed by the one or more processors: receive image data of one or more biological constructs; analyze the image data to generate processed image data via a data analytics module to recognize biomedical objects; and automatically annotate the processed image data to indicate a location of one or more biological objects within the one or more biological constructs.

The present invention discloses means and methods for detecting irregularities in the cells throughout a healthy tissue. The method generally relates to cancer detection, diagnosis and treatment, and more specifically pertains to detection, diagnosis and treatment guidance of cancerous or precancerous conditions through the use of thermal imaging technology and analysis.