Abstract: A system includes a storage and dispensing container for drug portions, a memory and a processor. The process executes instructions to detect by a first detection device rays of light transmitted past a drug portion by a first light emitting source in a first direction, to detect by a second detection device rays of light transmitted past the drug portion by a second light emitting source in a second direction different from the first direction, to generate, by the first and second detection devices, an image of the drug portion, to process the image using image analysis, to determine specified drug information for the drug portion and to identify, based on the determined drug information, a suitable singling device fitting the drug portions to be singularized and to be disposed within the storage and dispensing container. Additional systems for identifying a singling device are also provided.

Abstract: According to one embodiment, an X-ray diagnostic apparatus includes processing circuitry. The processing circuitry sets a plurality of ROIs in a first X-ray image. The processing circuitry calculates a statistical value for each ROI based on a plurality of values of pixels included in each ROI, thereby obtaining a plurality of statistical values. The processing circuitry acquires a threshold value for each statistical value. The processing circuitry determines a set of X-ray conditions relating to capturing of a second X-ray image subsequent to the first X-ray image based on the statistical value and the threshold value relating to each ROI.

Abstract: A first discrete Fourier transform unit generates a first frequency spectrum by two-dimensional discrete Fourier transform of an input image signal. A second discrete Fourier transform unit generates a second frequency spectrum by two-dimensional discrete Fourier transform of an output image signal output from a high frequency emphasis circuit. A first and second high frequency component index generators generate a first and second high frequency component indices indicating a total amount of high frequency components based on the first and second frequency spectra, respectively. A high frequency component emphasis ratio calculator calculates a high frequency component emphasis ratio which is a ratio between a first high frequency component index and a second high frequency component index. An emphasis amount controller generates an emphasis amount control value based on a high frequency component emphasis ratio and a target emphasis ratio, and supplies it to the high frequency emphasis circuit.

Abstract: Apparatuses, systems, and techniques to perform unsupervised keypoint or landmark learning using one or more neural networks. In at least one embodiment, one or more neural networks use pose and appearance information to construct a foreground and a background, which are then used to reconstruct an input image and determine loss values to train the one or more neural networks.

Abstract: Disclosed is a surface image guidance-based system for aligning and monitoring patient position including a first RGB depth optical camera positioned toward a table, on which a patient may lie, and configured to capture an image of a right side of the patient's body, a second RGB depth optical camera positioned toward the table, on which the patient may lie, and configured to capture an image of a left side of the patient's body, a central RGB depth optical camera positioned toward the table, on which the patient may lie, and configured to capture an image of a front side of the patient's body, and a central server configured to collect three-dimensional data of the patient on the basis of information captured in conjunction with the three cameras.

Abstract: A device includes software instructions for a circular plot analysis agent and at least one circular plot definition. The circular plot analysis agent obtains a digital image of a circular plot, detects a perimeter of the circular plot within the digital image, detects a plurality of edges within the perimeter, identifies a set of endpoints on the perimeter as a function of the plurality of edges, generates a plot descriptor from the set of endpoints, and initiates a transaction with a second device as a function of the plot descriptor.

Abstract: The present invention relates to computer-implemented method of predicting a deformation of at least one part of a patient body, a corresponding computer program, a program storage medium with such a program, as well as a medical system. The method comprises the steps of providing a computer simulatable biomechanical model of the at least one part of a patient body (step S1); carrying out S>1 forward simulations of the biomechanical model thereby calculating S resulting deformation vectors (step S2); calculating an approximated biomechanical model based on the S resulting deformation vectors (step S3); acquiring pre-operative data (step S4); optimizing, in a calculative manner, a deformation predicted by the approximated biomechanical model, which results in an optimized, predicted deformation vector (step S5); and deforming the pre-operative data by applying the optimized, predicted deformation vector to the pre-operative patient data (step S6).

Abstract: An X-ray computed tomography (CT) apparatus includes an X-ray tube configured to radiate X-rays; a detector configured to detect X-rays radiated by the X-ray tube and passed through a subject; a reconstruction processing unit configured to reconstruct imaging data output by the detector and generate CT image data and material decomposition image data; a first inference unit configured to perform inference on the material decomposition image data; a second inference unit configured to perform inference on the CT image data; a third inference unit configured to perform inference on composite image data; a selection unit configured to, in a case where an inference result of the first inference unit is different from an inference result of the second inference unit, select an inference result of the third inference unit; and a display control unit configured to cause the selected inference result to be displayed.

Abstract: A computer-implemented method and system of CT reconstruction can include receiving one or more CT projection images; performing smooth filtering on the one or more CT projection images to generate one or more smooth boundary filtered images; and back-projecting the one or more smooth boundary filtered images.

Abstract: A weakly supervised intracranial hemorrhage (ICH) detection workflow includes training a deep learning (DL) model including a coupled convolutional neural network and recurrent neural network on a large dataset of CT scans with expert-labeled slices indicating presence or absence of ICH. Transfer learning (TL) is used to further train the DL model using a second large dataset of CT scans with only scan labels extracted from radiology reports using natural language processing (NLP). The DL model weights each slice of the scan against the final ICH diagnosis using an attention-based bi-directional long-short term memory network, where the attention weights represent slice-level ICH predictions. Model-generated heatmaps highlight significant regions of the CT scans that lead to the provided ICH predictions.

Abstract: An information processing device, an information processing method, and a non-transitory computer-readable medium are disclosed. The information processing device includes a processor configured to acquire transverse tomographic images of a plurality of frames obtained by imaging a blood vessel of a patient, and calculate plaque burden in each of the frames by inputting the acquired transverse tomographic image of each of the frames to a model that has learned to calculate plaque burden in response to the transverse tomographic image inputted, and a display unit configured to display a longitudinal tomographic image based on the transverse tomographic images of the plurality of frames and a first object that is an object displayed in correlation with the longitudinal tomographic image and represents magnitude of the plaque burden at each position of the longitudinal tomographic images along an axial direction of the blood vessel.

Abstract: A system comprises an authentication sub-system configured to authenticate a user and a biochemical analysis sub-system configured to perform a biochemical analysis on a biological sample provided to the biochemical analysis sub-system. The system is configured to authenticate a user using the authentication sub-system, instruct an authenticated user to provide a biological sample to the biochemical analysis sub-system, execute a biochemical analysis on a provided biological sample.

Abstract: A method for training a 3D space consistent medical volume data generative model is provided, which is performed by one or more processors and which includes generating a synthetic volume data using a generator network, receiving real volume data, generating a plurality of 2D images based on the synthetic volume data and the real volume data, discriminating whether each of the plurality of generated 2D images is real or fake by using a plurality of discriminator networks, and updating the generator network based on discrimination results of the plurality of discriminator networks.

Abstract: A system and method for navigation of a luminal network including receiving a computed tomography (CT) image data set, generating a three-dimensional (3D) model, displaying the 3D model in a user-interface on a display operatively connected to the computing device, and receiving an indication of a location of a tumor in the CT image data set. The system and method further including displaying the location of the tumor in the 3D model, receiving an indication of a margin around the tumor to achieve a desired therapy, generating a pathway to the tumor for navigation of a catheter, receiving a location of a sensor associated with a navigation catheter and registering the CT image data set with a luminal network of a patient, displaying the location of the sensor within the 3D model that substantially corresponds to the location of the sensor within the luminal network of the patient.

Abstract: A system and method automatically detect, in MR images, WM lesions exhibiting a central vein sign. A set of MR images of a brain lesion is acquired, using images of the set as input to different ML algorithms. A first ML algorithm classifies inputted image(s) into first or second classes. The first class includes CVS+/? and the second class CVSe lesions. A second ML algorithm classifies inputted image(s) into third or fourth classes. The third class includes CVS+ lesions and the fourth central vein sign? lesions. For each set, probability values are used that the set belongs to classes as inputs to a final classifier performing a final classification of the set into second, third, or fourth classes. For each class, the final classifier outputs final probability that the set belongs to the class. The second, third or fourth class with highest probability value is provided through an interface.

Abstract: An image processing method and apparatus are provided. The method includes: acquiring a sample image, the sample image comprising an image obtained by image-capturing a sample within a preset optical waveband; processing a first image corresponding to at least one preset wavelength within the preset optical waveband in the sample image, to obtain a pseudo-color image; performing region division on the sample image according to a difference in sample element types in the sample image to obtain a region division result, the sample element types comprising a recognition element type; and determining an image region comprising the recognition element type from the sample image based on the pseudo-color image and the region division result.

Abstract: Systems and methods for generating attenuation maps for reconstructing medical images are disclosed. In some examples, measurement data, such as positron emission tomography (PET) data or single-photon emission computed tomography (SPECT) data, is received for a subject. A machine learning process is applied to the measurement data to generate initial synthetic images for multiple values of an imaging parameter. Further, patient data is received and classified to determine an object imaged with the subject. A second medical image is selected that includes the object, and a region-of-interest (ROI) of the initial synthetic images is determined. Further, based on the ROI an anatomical mask is generated for each initial synthetic image. A second image-to-image network process is applied to the patient data, the second medical image, a portion of each initial synthetic image that includes the ROI, and the corresponding anatomical mask to generate a final synthetic image.

Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Abstract: A computer-implemented method of evaluating a user image of a patient to enable identification of a historical twin of the patient. The method includes organizing a plurality of medical images in an archive and receiving from a medical professional each of: (i) a region of interest; (ii) a textual description; (iii) selections for binary criteria; and (iv) weights of weighable criteria. The method comprises using a natural language search to create a relevant set of medical images and creating an optimal set from the relevant set of medical images by discarding medical images from the relevant set based at least on the selections for binary criteria. The method includes image processing medical images in the optimal set using the weight of the features of the region of interest to create medical image results. The relevant set comprises less than ten percent of the medical images in the archive.

Abstract: Systems and methods for intraocular lens selection include receiving, by one or more computing devices implementing a prediction engine, pre-operative multi-dimensional images of an eye; extracting, by the prediction engine, pre-operative measurements of the eye based on the pre-operative images; estimating, by the prediction engine using a prediction model based on a machine learning strategy, a post-operative position of an intraocular lens based on the extracted pre-operative measurements; selecting a power of the intraocular lens based on the estimated post-operative position of the intraocular lens; and selecting the intraocular lens based on the selected power. In some embodiments, the systems and methods further include receiving post-operative multi-dimensional images of the eye after implantation of the selected intraocular lens, extracting post-operative measurements of the eye, and updating the prediction model based on the pre-operative measurements and the post-operative measurements.