Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images.

The present disclosure, in some embodiments, relates to a method of predicting stent expansion. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient and segmenting the pre-stent intravascular image to identify a lumen and a calcification lesion. A plurality of features are extracted from one or more of the lumen and the calcification lesion. A regression model is applied to one or more of the plurality of features to determine a minimum stent expansion metric (mSEM). The mSEM indicating how much a stent will expand after implantation. The mSEM is used to generate a classification of the blood vessel as an under-expanded area or a well-expanded area.

An image processing apparatus according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain volume data of a subject. The processing circuitry is configured to obtain base tubular object data by segmenting the volume data. The processing circuitry is configured to obtain small tubular object data from the volume data. The processing circuitry is configured to generate updated base tubular object data, on the basis of the small tubular object data and the base tubular object data. The processing circuitry is configured to output the updated base tubular object data.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

A method for establishing a 3D medical imaging model of a subject is to be implemented by an X-ray computed tomography (CT) scanner and a processor. The method includes: emitting X-rays on the subject sequentially from plural angles with respect to the subject to obtain M number of X-ray images of the subject in sequence; obtaining, for each pair of consecutive X-ray images, K number of intermediate image(s) by using the pair of consecutive X-ray images as inputs to a convolutional neural network (CNN) model that has been trained for frame interpolation; and establishing the 3D medical imaging model by using a 3D reconstruction technique based on the M number of X-ray images and the intermediate images obtained for the M number of X-ray images.

The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.

A neural network is trained for estimating patient hemodynamic data using a plurality of extravascular imaging data sets and a plurality of intravascular imaging data sets that are each co-registered to a corresponding extravascular imaging data set. A plurality of hemodynamic data sets are provided, each hemodynamic data set co-registered with the corresponding extravascular imaging data set. The neural network learns what hemodynamic data to expect for a given intravascular imaging data set. An intravascular imaging event is subsequently performed in which an intravascular imaging element is translated within a blood vessel of the patient to produce one or more intravascular images. The neural network uses its training to predict hemodynamic values corresponding to the one or more intravascular images from the intravascular imaging event, and the one or more intravascular images are outputted in combination with the predicted hemodynamic values.

An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

A system for displaying medical imaging data comprising one or more data inputs, one or more processors, and one or more displays, wherein the one or more data inputs are configured for receiving first image data generated by a first medical imaging device, wherein the first image data comprises a field of view (FOV) portion and a non-FOV portion, and the one or more processors are configured for identifying the non-FOV portion of the first image data and generating cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and additional information for display in a second portion of the display.