Disclosed is a method for calculating fractional flow reserve, comprising: collecting a pressure at the coronary artery inlet of heart by a blood pressure sensor in real-time, and storing a pressure value in a data linked table; obtaining an angiographic time according to the angiographic image, finding out the corresponding data from data queues based on time index using the angiographic time as an index value, screening out stable pressure waveforms during multiple cycles, and obtaining an average pressure Pa; and obtaining a length of the segment of blood vessel from angiographic images of the two body positions, and obtaining a blood flow velocity V; calculating a pressure drop ΔP for the segment of the blood vessel using the blood flow velocity V at the coronary artery inlet, and calculating a pressure Pd at the distal end of the blood vessel, and further calculating the angiographic fractional flow reserve.

There are provided a medical image processing apparatus, an endoscope apparatus, a diagnostic support apparatus, and a medical service support apparatus capable of detecting red blood cells using an endoscope image. A medical image processing apparatus includes: a medical image acquisition unit that acquires short wavelength medical images, which are medical images including a subject image and which are obtained by imaging a subject with light in a shorter wavelength band than a green wavelength band; and a red blood cell detection unit that detects red blood cells using the short wavelength medical images. The light in the short wavelength band is, for example, light in a blue band or a violet band of a visible range. The red blood cell detection unit detects, for example, a high-frequency, granular, and high-density region as red blood cells.

Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

A shape of a cell is favorably observed in a non-invasive manner. One aspect of a cell analysis device according to the present invention includes: holographic microscopy, an image generation unit configured to generate a phase image of a cell based on hologram data acquired by observing the cell with the holographic microscopy, a first machine learning model storage unit configured to store a cell area machine learning model generated by performing machine learning using training data in which a phase image of a cell generated based on the hologram data is used as an input image and a pseudo cell area image based on a stained image acquired by staining a cytoskeleton corresponding to the phase image is regarded as a ground truth image, and a cell area estimation unit configured to output a cell area estimation image indicating a cell area using a machine learning model stored in the first machine learning model storage unit, wherein the phase image generated by the image generation unit for the analysis target cell is used as an input image.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.

Methods and systems are provided for detecting coronary lesions in 3D cardiac computed tomography and angiography (CCTA) images using deep neural networks. In an exemplary embodiment, a method for detecting coronary lesions in 3D CCTA images comprises, acquiring a 3D CCTA image of a coronary tree, mapping the 3D CCTA image to a multi-label segmentation map with a trained deep neural network, generating a plurality of 1D parametric curves for a branch of the coronary tree using the multi-label segmentation map, determining a location of a lesion in the branch of the coronary tree using the plurality of 1D parametric curves, and determining a severity score for the lesion based on the plurality of 1D parametric curves.

Medical imaging systems and methods for determining contours of anatomical structures of vessels and vessel sections for representation in X-ray-based fluoroscopic images. Methods can include capturing a mask image, recording at least one filling image including a vascular tree filled with contrast agent, subtracting the mask image from the at least one filling image to produce at least one vessel image, generating a segmented vessel image by segmenting the vascular tree filled with contrast agent in the at least one vessel image and removing from the at least one segmented vessel image such vessels and vessel sections that do not meet a threshold value, wherein the threshold value represents a function of at least one predetermined structural feature, and superimposing the at least one segmented vessel image with a live X-ray image and reproduction on a display device.

Various aspects of the present disclosure generally relate to a multispectral sensor device. In some aspects, a sensor device may obtain, from image data collected by a sensor of the sensor device, first image data regarding a first measurement location of a measurement target, and may obtain, from the image data, second image data regarding a second measurement location of the measurement target, wherein the first measurement location and the second measurement location are sub-surface measurement locations within the measurement target. The sensor device may determine, based on the first image data and the second image data, a pulse transit time measurement, and provide information identifying the pulse transit time measurement. Numerous other aspects are provided.