Abstract: A medical image processing apparatus of embodiments includes processing circuitry. The processing circuitry is configured to acquire a data and a g-factor map. The data is generated as a result of a reception by an RF coil. The processing circuitry is configured to determine strength of denoise performed on the data on the basis of the g-factor map and perform the denoise on the data.

Abstract: The present invention relates to a method for reconstructing an image and an apparatus using the same Particularly, according to the method of the present invention, when a series of first slice images of a subject are inputted in a computing device, the computing device generates, from the first slice images, second slice images having a second slice thickness different from a first thickness, which is the slice thickness of the first slice image, and provides the generated second slice images.

Abstract: An information processing apparatus for processing an image includes circuitry unit and a communication interface. The circuitry acquires identification information to identify a set of one or more objects to be imaged by an imaging device according to a predetermined imaging sequence. The communication interface transmits at least one image of the set of one or more objects that is captured by the imaging device in association with the identification information, to a management device.

Abstract: An information processing apparatus for processing an image includes circuitry unit and a communication interface. The circuitry acquires identification information to identify a set of one or more objects to be imaged by an imaging device according to a predetermined imaging sequence. The communication interface transmits at least one image of the set of one or more objects that is captured by the imaging device in association with the identification information, to a management device.

Abstract: Embodiments related to methods of use of an image analysis system for identifying residual cancer cells after surgery are disclosed. In some embodiments, a patient-specific threshold used to detect abnormal cells in a surgical site can be determined. A medical imaging device can be configured to produce a set of images of an anatomy of a patient. An image analysis system, comprising one or more processors, can be configured to receive the set of images, and analyze the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

Abstract: A method, performed by a computer, for volumetrically determining a mid-sagittal plane (MSP) from a plurality of magnetic resonance (MR) images of a brain is disclosed. The plurality of MR images is received and converted into a 3D volume image defined in a 3D coordinate space. Semantic segmentation of an anterior commissure (AC) region, a posterior commissure (PC) region, and a third ventricle region in the 3D volume image is performed based on a pre-trained segmentation model to generate a 3D mask image labeled with the AC region, the PC region, and the third ventricle region. Based on the labeled 3D mask image, a 3D volumetric image of the segmented third ventricle region is constructed. The MSP is determined based on the 3D volumetric image of the third ventricle region.

Abstract: A medical image processing apparatus and a medical image processing method capable of improving structural component detectability with respect to a noise component in a medical image. The medical image processing apparatus includes: a separation unit that separates the medical image into a first component including a structural component as a main component and a second component including a noise component as a main component; a modulation unit that modulates the second component with a spatial frequency; and a correction unit that generates a corrected image based on the first component and the modulated second component.

Abstract: Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Abstract: For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.

Abstract: A method for generating synthetic X-ray images is provided. A first neural network is provided to generate at least one synthetic X-ray image having specified quality. A second neural network is provided to ascertain characterizing properties from at least one secondary X-ray image for the first neural network. The first neural network and the second neural network may be trained by primary X-ray images of specified minimum quality. The at least one secondary X-ray image has a lower quality compared to primary X-ray images. The at least one synthetic X-ray image is generated with the aid of the provided characterizing properties by the first neural network. The at least one synthetic X-ray image is improved with regard to quality compared to the at least one secondary X-ray image.

Abstract: A method of facilitating processing of pathology images involves receiving pathology image data representing a pathology image having a plurality of image regions, wherein the pathology image data includes, for each of the plurality of image regions, a respective plurality of representations of the image region including a first representation and a second representation, the second representation having a smaller data size than the first representation. The method involves, for each of the plurality of image regions: determining, based at least in part on the first representation of the image region, a first set of image properties, determining whether the first set of image properties meets first image property criteria, and, if the first set of image properties meets the first image property criteria, producing signals for causing the second representation to be used in place of the first representation. Other methods, systems, and computer-readable media are disclosed.

Abstract: Fluorescence videostroboscopy imaging is described. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation. The system includes a controller configured to cause the emitter to emit the pulses of electromagnetic radiation at a strobing frequency determined based on a vibration frequency of vocal cords of a user. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises electromagnetic radiation having a wavelength from about 795 nm to about 815 nm.

Abstract: A list of images is received. The images were captured by a sensor of an ADV chronologically while driving through a driving environment. A first image of the images is identified that includes a first object in a first dimension (e.g., larger size) detected by an object detector using an object detection algorithm. In response to the detection of the first object, the images in the list are traversed backwardly in time from the first image to identify a second image that includes a second object in a second dimension (e.g., smaller size) based on a moving trail of the ADV represented by the list of images. The second object is then labeled or annotated in the second image equivalent to the first object in the first image. The list of images having the labeled second image can be utilized for subsequent object detection during autonomous driving.

Abstract: An apparatus for recording and displaying a three-dimensional OCT video includes an OCT system and a data processing unit configured to combine OCT volumes recorded sequentially in time to form a combined OCT volume, to generate display data which facilitate the display of the combined OCT volume, to register a current OCT volume of the OCT volumes, to combine the registered current OCT volume with the previous combined OCT volume to generate an updated combined OCT volume, to carry out a check for combining the registered current OCT volume with the previous combined OCT volume and in respect of which volume regions of the current OCT volume have changed in comparison with the corresponding volume regions of the previous combined OCT volume, and only to combine those volume regions of the current OCT volume that have not changed with the corresponding volume regions of the previous combined OCT volume.

Abstract: Systems and methods include a reduced computation, computer-based management of point-of-care (POC) on-site ultrasound (ULS) imaging resources. At a digital computation resource of a POC site, a ULS tissue reflection sample data is received from a hand-held ULS scanning device. A digital computation resource of the POC site, applies a reduced computation tissue ULS reflection speckle noise (SN) physics model—blurring noise (BN) physics model based deep learning (DL) trained convolutional neural network (CNN) denoising process to the ULS tissue reflection sample data outputting an estimated denoised ULS tissue reflection image data. A visual rendering of the estimated denoised ULS tissue reflection image data is displayed on a display resource of the POC site.

Abstract: System, method and computer readable medium that assess characteristics of an embryo by processing video image data of the embryo. Video is obtained, typically from third parties, of a target embryo. The video has frame speed of two frames per second, or faster and includes image data representing morphokinetic movement of the embryo. The image data is processed using a trained machine learning model that assesses embryo characteristics. The video has a duration of ten minutes or less. The assessment can be a prediction of a likelihood the embryo is viable and/or will produce a pregnancy upon transfer into a recipient. Further, the assessment can predict a likelihood the embryo will: (1) produce offspring having a specific sex; (2) embody a genetic anomaly; (3) perpetuate desired traits in produced offspring and/or (4) produce undesired characteristics in produced offspring.

Abstract: An image processing apparatus sets a maximum size of an object that is to be included in a detection result of detection processing to detect the object from an image captured by an imaging unit, and sets a second region based on a position of a first region, designated by a user, in the image and the set maximum size. The second region, larger than the first region, includes the first region, and is subjected to the detection processing.

Abstract: An image interpretation support apparatus includes: an acquisition unit that acquires a plurality of projection images obtained by tomosynthesis imaging in which a radiation is irradiated to a breast from different irradiation angles by a radiation source and a projection image is captured at each irradiation angle by a radiation detector; a first generation unit that generates a plurality of tomographic images on each of a plurality of tomographic planes of the breast from the plurality of projection images; a second generation unit that generates a synthetic two-dimensional image from a plurality of images among the plurality of projection images and the plurality of tomographic images; a detection unit that detects an object of interest candidate region estimated to include an object of interest from the synthetic two-dimensional image; and a determination unit that determines whether or not the object of interest is included in the object of interest candidate region on the basis of the plurality of tomog

Abstract: The present invention relates to a deep learning-based multiple skin lesion detection system, a multiple lesion detection method, and a computer-readable recording medium that has a program for implementing same recorded thereon. The system according to the present invention enables accurate classification and detection of various skin lesions having similar characteristics, on the basis of a context-dependent decision-making structure in which the local spatial correlation between various skin lesions in skin is considered.

Abstract: An apparatus for analysing a head CT scan, where the head CT scan includes a plurality of cross-sections, includes a processor configured to select a first cross-section from among the plurality of cross-sections in the head CT scan; and analyse the first cross-section to determine whether the whole head CT scan was taken with contrast. The analyzing includes determining whether the first cross-section shows contrast, based on a presence of bright patches in the first cross-section, determining whether an amount of sulci in the first cross-section is below a threshold amount. In response to determining that the first cross-section shows contrast, determine that the whole head CT scan was taken with contrast, and in response to the determining that the first cross-section does not show contrast and that the amount of sulci is below the threshold amount determining that the whole head CT scan was taken without contrast.