Abstract: Aspects of the invention include ultrasound systems that suppress grating lobe artifacts arising due to high frequency operation of an array transducer probe which is operated at a frequency higher than its pitch limitation.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patient has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

Abstract: An information processing apparatus according to the invention includes: an acquisition unit that acquires a two-dimensional image of a person and a three-dimensional registered image of a registrant; and a generation unit that, when superimposing the two-dimensional image and the three-dimensional registered image to generate a composite image, performs an editing process for changing an appearance of the person or the registrant for at least one of the two-dimensional image, the three-dimensional registered image, and the composite image.

Abstract: A neural network is trained to segment interferogram images. A first plurality of interferograms are obtained, where each interferograms corresponds to data acquired by an OCT system using a first scan pattern, annotating each of the plurality of interferograms to indicate a tissue structure of a retina, training a neural network using the plurality of interferograms and the annotations, inputting a second plurality of interferograms corresponding to data acquired by an OCT system using a second scan pattern and obtaining an output of the trained neural network indicating the tissue structure of the retina that was scanned using the second scan pattern. The system and methods may instead receive a plurality of A-scans and output a segmented image corresponding to a plurality of locations along an OCT scan pattern.

Abstract: In one embodiment, a fluorescence histo-tomography (FHT) system is disclosed. The FHT system includes an imaging housing, a fluorescence camera located within the housing, a white light camera located within the imaging housing, and a fluorescence light source located within the imaging housing. The FHT system further includes a support mount configured to support the imaging housing within a chamber of a slicing apparatus such that the cameras and fluorescence light source are aimed towards a block face of a tissue specimen retained within the chamber.

Abstract: A method and a device of extracting a label in a medical image are provided. The method includes: performing an edge detection on the medical image by using an edge detection algorithm, to acquire edge information in the medical image; determining at least one target area defined by the edge information; performing a fitting process on the at least one target area, to obtain a fitting area; and extracting the label in the medical image by selecting, from the fitting areas, at least one target fitting area matching a preset condition, wherein the preset condition is set based on a characteristic of the fitting area.

Abstract: A method for dynamically evaluating a tear fluid layer using an interference fringe image of the tear fluid layer composed of a plurality of consecutive frames, and includes: an image creation step of creating at least one break detection image of a first break detection image, a second break detection image, a third break detection image, and a fourth break detection image, which are images for detecting a breaking site of the tear fluid layer; a determination step of determining whether the break detection image created by the image creation step corresponds to a predetermined breakup pattern; a tally step of tallying the determination results determined by the determination step; and an evaluation step of evaluating, on the basis of a tallied result by the tally step, a breakup pattern of the interference fringe image of the tear fluid layer.

Abstract: Introduced here are diagnostic platforms able to optimize computer-aided diagnostic (CADx) models by simulating the optical performance of an imaging device based on its mechanical components. For example, a diagnostic platform may acquire a source image associated with a confirmed diagnosis of a medical condition, simulate optical performance based on design data corresponding to a virtual prototype of the imaging device, generate a training image by altering the source image based on the optical performance, apply a diagnostic model to the training image, and then determine whether the performance of the diagnostic model meets a specified performance threshold. If the diagnostic model fails to meet the specified performance threshold, the diagnostic platform can automatically optimize the diagnostic model for the imaging device by altering its underlying algorithm(s).

Abstract: A CPU needs to perform reset operation when a secondary arithmetic processing unit controlled by the CPU controls a signal processing circuit. CPU A controls module A. CPU B controls module B. Module A and module B control a signal processing circuit. CPU A and CPU B issue a reset request to the signal processing circuit. The signal processing circuit performs a reset process based on the reset request accepted from the CPU and a control origin identification signal that identifies a CPU as an origin of controlling the module having started a signal processing section.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: performing iterative image reconstruction of imaging data acquired using an image acquisition device (12); selecting an update image from a plurality of update images produced by the iterative image reconstruction; processing the selected update image to generate a hot spot artifact map; and suppressing hot spots identified by the generated hot spot artifact map in a reconstructed image output by the iterative image reconstruction.

Abstract: A method for adaptive treatment planning is provided. The method may include obtaining a planning image volume of a subject, a treatment image volume of the subject, and a first treatment plan related to the planning image volume of the subject, each of the planning image volume and the treatment image volume including an ROI of the subject. The method may also include registering the planning image volume and the treatment image volume, and determining a first contour of the ROI in the registered planning image volume and a second contour of the ROI in the registered treatment image volume. The method may also include evaluating whether an error exists in at least one of the registration or the contour determination based on the first contour and the second contour, and determining a second treatment plan with respect to the treatment image volume based on the evaluation result.

Abstract: A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.

Abstract: Disclosed is a method for vein recognition, the method includes: performing a difference operation and a channel connection on two to-be-verified target vein images respectively to obtain a difference image and a two-channel image of the two target vein images; performing the channel connection on the obtained difference image and two-channel image to obtain a three-channel image, so as to use the three-channel image as an input of a CNN network; fine-tuning a pre-trained model SqueezeNet that completes training on an ImageNet; integrating the difference image and the three-channel image through a cascade optimization framework to obtain a recognition result; regarding a pair of to-be-verified images as a sample, transforming the sample, taking the transformed sample as the input of the CNN network, obtaining a recognition result by supervised training on the network.

Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.

Abstract: A method and device for stratified image segmentation are provided. The method includes: obtaining a three-dimensional (3D) image data set representative of a region comprising at least three levels of objects; generating a first segmentation result indicating boundaries of anchor-level objects in the region based on a first neural network (NN) model corresponding to the anchor-level objects; generating a second segmentation result indicating boundaries of mid-level objects in the region based on the first segmentation result and a second NN model corresponding to the mid-level objects; and generating a third segmentation result indicating small-level objects in the region based on the first segmentation result, a third NN model corresponding to the small-level objects, and cropped regions corresponding to the small-level objects.

Abstract: The present disclosure relates to a pupil positioning method. The pupil positioning method may include: obtaining an eye image under illumination of a light source; determining a first internal point in a pupil of the eye image; calculating gradient changes of pixel points along a straight line starting from the first internal point toward outside of the pupil; determining a plurality of edge points at an edge of the pupil based on the gradient changes of the pixel points along the straight line; and performing ellipse fitting on the edge points to obtain a pupil center.

Abstract: Systems and methods for generating a synthesized contrast enhanced medical image are provided. An input medical image is received. A synthesized contrast enhanced medical image is generated based on the input medical image using a trained machine learning based generator network. The synthesized contrast enhanced medical image includes one or more synthesized contrast enhanced regions of pathological tissue. The synthesized contrast enhanced medical image is output.

Abstract: Methods, systems, apparatus, and computer programs, for processing images through multiple neural networks that are trained to detect a pancreatic ductal adenocarcinoma. In one aspect, a method includes actions of obtaining a first image that depicts a first volume of voxels, performing coarse segmentation of the first image using a first neural network trained (i) to process images having the first volume of voxels and (ii) to produce first output data, determining a region of interest of the first image based on the coarse segmentation, performing multi-stage fine segmentation on a plurality of other images that are each based on the region of interest of the first image to generate output data for each stage of the multi-stage fine segmentation, and determining based on the first output data and the output data of each stage of the multi-stage fine segmentation, whether the first image depicts a tumor.

Abstract: One or more features of a friction ridge signature of a subject may be identified based on information representing a three-dimensional topography of friction ridges of the subject. Information representing the three-dimensional topography of the friction ridges of the subject may be received. One or more level-three features of the friction ridge signature of the subject may be identified based on the information representing the three-dimensional topography of the friction ridges of the subject. The one or more level-three features may include one or more topographical ridge peaks, topographical ridge notches, topographical ridge passes, pores, and/or other information.

Abstract: An apparatus for vascular modeling is disclosed. The apparatus receives medical images from an imaging device that include representations of a coronary vessel tree of a subject recorded at a different viewing angles. The apparatus determines, from a first of the medical images, a first centerline set and first vessel diameters for sample points along the first centerline set, and determines, from a second of the medical images, a second centerline set and second vessel diameters for sample points along the second centerline set. The apparatus determines a correspondence between the first centerline set and the second centerline set, and determines diameters for a combined centerline set based on the correspondence of sample points along the first and second centerline sets. The apparatus provides the combined centerline set for estimating blood flow resistance values of the coronary vessel tree of the subject to determine at least one potential stenosis.