Abstract: Disclosed is a method, a computer readable storage medium and an apparatus for processing medical image data. Input medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system on the input medical image data to identify a volume of interest within which an instance of a predetermined anatomical structure is located. First and second determination processes are performed at the data processing system to determine, respectively, first and second anatomical directions for the instance of the anatomical structure that are defined relative to the coordinate system of the input medical image data. Output data relating to the first and second anatomical directions is output from the data processing system.

Abstract: To provide a medical image processing apparatus and a medical image processing method enabling a reduction in burden of re-imaging and re-interpretation of a medical image. The medical image processing apparatus for processing a medical image includes: a detection section that detects, from the medical image, a candidate region which is a region including a lesion candidate or target tissue, and that calculates an identification score for the candidate region; a designated region acquisition section that acquires a designated region which is a region designated by an operator; and a redetection section that calculates an identification score for the designated region based on the designated region and multiple predefined regions which are regions used in a process of detecting the candidate region.

Abstract: A medical image processing apparatus includes an acquisition unit and a processing unit. The acquisition unit acquires volume data of a subject. The processing unit displays a three-dimensional image by rendering the acquired volume data, on a display unit. The processing unit displays a first object showing (i) a point on a body surface of the subject and (ii) a direction to the volume data in the three-dimensional image, and displays a two-dimensional image of a surface including the point on the body surface and being defined based on the direction, in the volume data. The processing unit acquires information of a first operation to change display of the two-dimensional image, and moves the point on the body surface along the body surface of the subject based on the first operation to update display of the first object and the two-dimensional image.

Abstract: A discrete wavelet transform-based noise removal apparatus and a remote medical diagnosis system using the same. The discrete wavelet transform-based noise removal apparatus can effectively remove noise from various medical images including ultrasound images, images indicative of results of biological or chemical reaction on a bio-disc, images obtained by medical devices, and images for telemedicine.

Abstract: A method and system is disclosed for displaying acquired image data of a subject. The image may include a high resolution of clear image based on the acquired image data. The image may assist in evaluating the subject.

Abstract: A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MIP image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MIP image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

Abstract: The present disclosure provides a system and method for medical imaging. The method may include obtaining a preliminary image and scanning data of a subject acquired using a scanner. The method may also include determining a regularization parameter for a regularization item of an objective function based at least in part on the scanning data, wherein the regularization parameter includes at least two of a first component characterizing quality of the scanning data, a second component characterizing the scanner, or a third component characterizing a feature of the subject. The method may further include generating an image of the subject by reconstructing the preliminary image based on the objective function.

Abstract: The most appropriate image for a diagnostic target among a plurality of images is selected and accurate diagnosis support information is presented regardless of the type of a selected image, a modality, or the like. An image diagnosis support apparatus includes: a diagnostic information generation unit that generates diagnostic information based on a plurality of medical images; a reliability calculation unit that evaluates an image quality and calculates an image reliability for each of the plurality of medical images; and a degree-of-contribution calculation unit that calculates a degree of contribution of each of the plurality of medical images to the diagnostic information using an internal parameter indicating a degree of appropriateness of each medical image for a diagnostic target and the reliability calculated by the reliability calculation unit. An image for detection used by the diagnostic information generation unit is generated based on the degree of contribution.

Abstract: There are provided a medical image processing device, and endoscope system, a diagnosis support method, and a program which can support diagnosis by avoiding an inappropriate report in a case where any of site information of an observation target and lesion type information detected from a medical image is incorrect. The medical image processing device includes at least one processor. The at least one processor acquires the medical image, acquires the site information indicating a site of an observation target included in the medical image, in a human body, detects a lesion from the medical image to acquire the lesion type information indicating a lesion type, determines presence or absence of a contradiction between the site information and the lesion type information, and decides a report mode of the site information and the lesion type information on the basis of a determination result.

Abstract: A route selection assistance system enabling easy selection of a route of a living body lumen for delivering a medical instrument to a site within a living body via the living body lumen, a recording medium on which a route selection assistance program is recorded, and a route selection assistance method are disclosed. The route selection assistance system includes: a receiving section configured to receive an input of site information specifying a target site; an image obtaining section configured to obtain image information on a living body of a target patient; a route extracting section configured to extract a plurality of routes of a living body lumen; a ranking assigning section configured to assign rankings to the plurality of routes according to ease of delivery of the medical instrument and patient scores determined according to magnitude of a burden imposed on the target patient.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

Abstract: A mechanism is provided in a data processing system for refining lesion contours with combined active contour and inpainting. The mechanism receives an initial segmented medical image having organ tissue including a set of object contours and a contour to be refined. The mechanism inpaints object voxels inside all contours of the set. The mechanism calculates an updated contour around the contour to be refined based on the in-painted object voxels to form an updated segmented medical image. The mechanism determines whether the updated segmented medical image is improved compared to the initial segmented medical image. The mechanism keeps the updated segmented medical image responsive to the updated segmented medical image being improved.

Abstract: An automated method and system for determining the risk of developing a cancer in a subject, the method comprising preparing a tissue sample obtained from the subject for visually identifying at least one biological marker associated with the cancer, digitally scanning the prepared tissue sample, analyzing the scanned image of the tissue sample to identify regions of interest, quantifying at least one parameter associated with the marker, and executing an algorithm using the quantified parameter to calculate a risk score, wherein the risk score is representative of the risk of the individual developing the cancer.

Abstract: The present application related to a method for detecting an object image using a convolutional neural network. Firstly, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the sample image, the detected object image is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the detected object image.

Abstract: Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

Abstract: In inspection information display device, method, and program includes a display controller displays data related to a patient, for example, an analysis result obtained by analyzing a medical image and inspection information related to the data on a display, a decision unit decides at least one of a necessary inspection or a necessary treatment for confirming the analysis result, a resource information acquisition unit acquires resource availability information for executing at least one of the necessary inspection or the necessary treatment for confirming the analysis result, and a display controller 31 further displays the resource availability information on the display.

Abstract: A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

Abstract: A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

Abstract: Methods and systems for classifying an image.

Abstract: A medical image processing apparatus comprises processing circuitry configured to: obtain image data representative of a brain of a subject; obtain data representing a clinical sign or symptom of the subject, wherein the clinical sign or symptom is relevant to a brain condition; process the image data to obtain an estimation of an abnormality in the brain of the subject; and determine whether the estimation of the abnormality is consistent with the data representing the clinical sign or symptom.