The present invention relates to a guidance for treatment of a chronic total occlusion. In order to provide improved guidance information for chronic total occlusion treatment, a device (10) for guidance for treatment of a chronic total occlusion is provided that comprises an image supply (12), a data processor (14) and an output (16). The image supply provides a sequence of angiographic images comprising a vascular structure. The data processor detects at least one portion of the vascular structure indicating a total occlusion of a vessel based on the sequence of angiographic images; and determines an image of the sequence of images that shows at least one segment of the vessel next to the total occlusion; and generates guidance image data based on the determined image. The output provides the generated guidance image data. Thus, additional information relating to spatial aspects is provided to the user based on 2D image data.

A non-transitory computer-readable recording medium stores therein an examination assisting program that causes a computer to execute a process including: performing a site detecting process that uses an object detection technique on each of a plurality of ultrasound examination images taken of an examined subject by performing a scan on the examined subject; and displaying a site detection map in which a detection result of each of a plurality of sites included in the examined subject is kept in correspondence with the scan, on a basis of detection results from the site detecting process.

An image generation system comprising a computer processor that functions as: an image input part configured to input an input image, the input image being a time-series image obtained by imaging an observed cell over time; and an image generator configured to generate a growth prediction image of the observed cell from the time-series image of the observed cell based on a first learned model, which has learned a relationship between the time-series image of a learning cell and a feature of the learning cell, and output the growth prediction image as an output image.

A brain tumor image segmentation method and device are disclosed. The disclosed method includes acquiring a basic white matter template generated based on brain magnetic resonance images of a plurality of healthy samples, collecting corresponding low, mid and high b-value diffusion weighted images of the brain of a patient, segmenting out a tumor region including the tumor body and the edema on each image based on the signal distribution of each image in a first set image group of the patient, removing the normal white matter region from the tumor region according to the basic white matter template and the high b-value diffusion weighted image, and classifying the value of the voxel in each image in a second set image group and a second apparent diffusion coefficient image obtained through calculations to obtain a tumor body region and an edema region.

In one aspect of the present application, a method for generating image of orthodontic treatment outcome using artificial neural network is provided, the method comprises: obtaining a picture of a patient's face with teeth exposed before an orthodontic treatment; extracting a mouth mask and a first set of tooth contour features from the picture of the patient's face with teeth exposed before the orthodontic treatment using a trained feature extraction deep neural network; obtaining a first 3D digital model representing an initial tooth arrangement of the patient and a second 3D digital model representing a target tooth arrangement of the patient; obtaining a first pose of the first 3D digital model based on the first set of tooth contour features and the first 3D digital model; obtaining a second set of tooth contour features based on the second 3D digital model at the first pose; and generating an image of the patient's face with teeth exposed after the orthodontic treatment using a trained deep neural network for generating images, based on the picture of the patient's face with teeth exposed before the orthodontic treatment, the mask and the second set of teeth contour features.

A system and method for displaying images of internal anatomy includes an image processing device configured to provide high resolution images of the surgical field from low resolution scans during the procedure. The image processing device digitally manipulates a previously-obtained high resolution baseline image to produce many representative images based on permutations of movement of the baseline image. During the procedure a representative image is selected having an acceptable degree of correlation to the new low resolution image. The selected representative image and the new image are merged to provide a higher resolution image of the surgical field. The image processing device is also configured to provide interactive movement of the displayed image based on movement of the imaging device, and to permit placement of annotations on the displayed image to facilitate communication between the radiology technician and the surgeon.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

Angiographic data is obtained by injecting a chemical contrast agent intravascularly, and imaging passage of the contrast as a function of time, thereby generating a sequence of images. To correct error from uncalibrated timestamps embedded in the image metadata, radio-opaque markers are used to generate a watermark embedding timestamp data in obtained images. The radio-opaque markers cause opacification on the x-ray images in the form of dynamic watermarks that encode timestamps. The positions of the markers in the watermark (cast from the radio-opaque markers) are then processed and analyzed to generate an accurate timestamp for the image. By generating an accurate timestamp, synchronized calculations of the images with other data sources are provided.

Among the various aspects of the present disclosure is the provision of methods and systems for real-time 3D MRI that combines dynamic keyhole data sharing with super-resolution imaging methods to improve real-time 3D MR images in the presence of motion.

A method for phase correction in proton resonance frequency (PRF) thermometry application includes acquiring a series of magnetic resonance (MR) images comprising a first MR image and plurality of subsequent MR images depicting an anatomical area of interest. The MR images are acquired while tissue in the anatomical area of interest is undergoing a temperature change. Each subsequent MR image is registered to the first MR image to yield a plurality of registered images. A plurality of basis images are computed from the registered images using Principal Component Analysis (PCA). The basis images are used to remove motion-related phase changes from a second series of MR images, thereby yielding a motion corrected second series of MR images. One or more temperature difference maps are generated that depict a relative temperature change for the tissue in the anatomical area of interest based on the motion corrected second series.