Abstract: Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Abstract: According to one embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry acquires medical data, and generates an imaging parameter by inputting the medical data to a trained model, the imaging parameter being a parameter of a medical image diagnostic apparatus with respect to the medical data, the trained model being trained to generate an imaging parameter of the medical image diagnostic apparatus based on medical data.

Abstract: An inverse visualization of a time-resolved angiographic image data set of a vascular system of a patient that was recorded by a medical imager during the flow of a contrast medium through the vascular system is provided. The time-resolved angiographic image data set of the vascular system has a temporal sequence of frames of the vascular system corresponding to the contrast medium filling process. A data set from bolus arrival times for each pixel or voxel is determined. The bolus arrival time corresponds to the time in the temporal sequence at which a predetermined contrast enhancement due to the contrast medium filling first occurs. A data set of temporally inverted bolus arrival times with respect to the contrast medium filling is determined for each pixel or voxel, resulting in a temporally inverted sequence of frames with respect to the contrast medium filling. The time-resolved angiographic image data set in the temporally inverted sequence is visualized.

Abstract: Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

Abstract: An imaging system includes a microscope to generate magnified images of regions of interest of a tissue sample, a camera to capture and store the magnified images, and a controller. The controller is configured to, for each magnification level in a sequence of increasing magnification levels, image one or more regions of interest of the tissue sample at the current magnification level. For each region of interest, data is generated defining one or more refined regions of interest based on the magnified image of the region of interest of the tissue sample at the current magnification level. Each refined region of interest corresponds to a proper subset of the tissue sample, and the refined regions of interest of the tissue sample provide the regions of interest to be imaged at a next magnification level from the sequence of increasing magnification levels.

Abstract: A method for automatically training and applying automatic segmentation in digital image processing is provided. The method may include, in response to receiving a plurality of digital images wherein each digital image associated with the plurality of digital images comprises only one annotated structure out of a plurality of structures included in each digital image, applying a predictive algorithm to each digital image that determines a predicted probability of each annotation in each digital image, determines a predicted background for each digital image, and merges the predicted probability of each annotation with the predicted background in each digital image. The method may further include, in response to applying the predictive algorithm, using the received plurality of digital images to train and apply an application for automatically segmenting unlabeled digital images.

Abstract: A device is provided that may include an illuminator operable to interrogate at least a lens of an eye. The illuminator may include at least one light source and a lens positioned with respect to the light source to produce interrogating radiation. The device also may include a detector operable to image the total autofluorescence response of the lens of the eye as viewable through a pupil of the eye, the detector comprising an image sensor. The device can also include a controller operable to control operation of the illuminator and the detector. The controller may interrogate at least the lens of the eye by activating the illuminator for a select time, obtain at least one image of the total autofluorescence response of the lens at the detector during or immediately subsequent to the select time, and transmit the at least one image to a remote device.

Abstract: Various methods and systems are provided for reducing blur in a diagnostic image. In one example, a method includes reducing blur in a diagnostic image by applying a deconvolution filter to the diagnostic image, the deconvolution filter generated from a point spread function (PSF) estimation of blur at each pixel of the diagnostic image, the PSF estimation generated based on a motion vector field between the diagnostic image and a pre-shot image acquired prior to the diagnostic image.

Abstract: In one embodiment, a computer-implemented method provides a prediction of a medical target variable. The computer-implemented method includes receiving medical imaging data of an examination area, the examination area including a plurality of lesions of an anatomical structure, wherein each lesion of the plurality of lesions of the anatomical structure spaced apart from any other lesion of the plurality of lesions of the anatomical structure; calculating a spread parameter based on the medical imaging data, the spread parameter being indicative of a spread of a spatial distribution of the plurality of lesions of the anatomical structure; calculating the prediction of the medical target variable based on the spread parameter; and providing the prediction of the medical target variable.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for annotating magnetic resonance imaging (MRI) images. The method may include receiving mammogram (MG) image data representing annotated MG images of a patient breast, the annotated MG images being one of either a craniocaudal view or of a mediolateral oblique view. The method may include identifying annotations representing an abnormality at a first location in the annotated MG images; receiving MRI image data representing MRI images of the patient breast; generating annotated MRI image data using the MRI image data and the annotations identified in the annotated MG images, the annotated MRI image data including MRI annotations at a second location based at least in part on the first location, the MRI annotations in the annotated MRI image data representing the abnormality; and storing the annotated MRI image data in a database.

Abstract: The disclosure provides a system that may acquire, via an image sensor, an image of an eye of a person; may determine a location of an iris of the eye from the image; may determine a position of a suction ring from the image; may display, via a display, the image; may display, via the display, a first graphic overlay on the image that indicates the location of the iris of the eye; may display, via the display, a second graphic overlay on the image that indicates the position of the suction ring; may determine multiple iris structures from the image; may determine an orientation of the eye based at least on the multiple iris structures from the image; and may display, via the display, information that indicates the orientation of the eye.

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: 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: The present disclosure relates to a method and a system of quantitative intravascular optical coherence tomography.

Abstract: Disclosed herein are computer systems for, in part, image processing. Also disclosed herein are systems for processing ocular images of multiple imaging modalities to detect ocular diseases. Also disclosed herein are method comprising systems as described herein.

Abstract: A system includes an electrical tomography system and a volume estimation system. The volume estimation system is configured to reconstruct an initial impedance image based at least partially on received electrical tomography data of a domain, receive prior information associated with the domain, enhance the initial impedance image based at least partially on the received prior information to generate an enhanced impedance image, and based at least partially on the enhanced initial impedance image, generate a volumetric image of a region of interest of the enhanced impedance image, wherein the volumetric image represents a plurality of values indicating a volume of a fluid.

Abstract: A display control apparatus that performs rendering on volume data generated based on a signal obtained by receiving a photoacoustic wave generated by an object irradiated with light includes a first unit configured to obtain positional information of a surface of the object, a second unit configured to set a region inside the object between a surface located at a first distance from the surface of the object and a surface located at a second distance from the surface of the object as a display region on the basis of the positional information of the surface of the object, and a third unit configured to perform the rendering on the volume data in a manner that the display region and a region except for the display region can be discriminated from each other.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire image data, a calculation unit configured to calculate, for each unit region of the image data, a confidence that the unit region is an extraction subject, the confidence as the extraction subject being calculated for each unit region of the image data by inputting the image data into a trained model of a neural network that has been trained using images of an existing extraction subject or a region of interest as training data, a modification unit configured to modify a reference value of the confidence, which is used to extract a region of interest, an extraction unit configured to extract the region of interest on the basis of the calculated confidence of each unit region and the modified reference value, and a display unit configured to display the extracted region of interest.

Abstract: A method for determining the relative position between a first camera and a second camera used in a medical application, wherein the first camera captures a 2D image of a phantom, the second camera emits light onto the phantom and analyzes the reflected light, thus generating a 3D point cloud representing points on the surface of the phantom, and the phantom has a planar surface forming a background on which a plurality of 2D markers are formed, wherein one of the background and the 2D markers is reflective, thus reflecting light emitted by the second camera back to the second camera, and the other one is non-reflective, thus not reflecting light emitted by the second camera back to the second camera, the method involving that a) the first camera captures a 2D image of the phantom, b) the second camera generates a 3D point cloud representing the planar surface of the phantom, c) the 2D markers are identified in the 2D image, thus obtaining 2D marker data representing the locations of the 2D markers in the 2D

Abstract: This invention provides a signal-processing method that makes it possible to acquire, relatively easily and surely, a highly reliable normalized impulse-response signal without relying on the signal-correction processing after normalization. The signal-processing method of this invention includes a low-frequency extraction step, a high-frequency extraction step and a synthesizing step. In the low-frequency extraction step, only the low-frequency component is extracted from the spectrum of the first normalized signal NS1 obtained by normalizing the target signal Stgt in the time domain. In the high-frequency extraction step, only the high-frequency component is extracted from the spectrum of the second normalized signal NS2 obtained by normalizing the target signal Stgt in the frequency domain using the reference signal Sref.