Abstract: Methods are provided for the automated detection, characterization, and selection of micro-objects in a microfluidic device. In addition, methods are provided for grouping detected micro-objects into subgroups that share the same characteristics and, optionally, repositioning micro-objects in a selected sub-population within the microfluidic device. For example, micro-objects in a selected sub-population can be moved into sequestration pens. The methods also provide for visual displays of the micro-object characteristics, such as two- or three-dimensional graphs, and for user-based definition and/or selection of sub-populations of the detected micro-objects. In addition, non-transitory computer-readable medium in which a program is stored and systems for carrying out any of the disclosed methods are provided.

Abstract: A method of operation of a compute system includes: generating a digital model from a input image; segmenting a 3D-polygon from the input image by applying a triangular mesh; assembling a 3D surface, based on the digital model of a zone of interest from the 3D-polygon; calculating an average threshold value of pixels within the zone of interest for identifying a condition over the 3D surface; and assembling a skin condition display from analyzing by a skin artificial intelligence (AI) of the 3D surface for displaying on a device.

Abstract: An image processing method and apparatus, a device, a video processing method and a storage medium are provided. The image processing method includes: receiving an input image; and processing the input image by using the convolutional neural network to obtain an output image. A definition of the output image is higher than a definition of the input image. Processing the input image by using the convolutional neural network to obtain the output image includes: performing feature extraction on the input image; concatenating the input image and the plurality of first images; performing the feature extraction on the first image group; fusing the plurality of second images and the plurality of first images; concatenating the input image and the plurality of third images to obtain a second image group; and performing the feature extraction on the second image group to obtain the output image.

Abstract: A computer-implemented method and system performs segmentation on at least a portion of the digital jaw to provide one or more digital surface segments, determines a digital tooth center for at least one digital tooth in the digital jaw, determines a digital tooth area around the digital tooth center, deletes one or more surface segments not intersecting with the digital tooth area; and smooths a mesh boundary.

Abstract: The present disclosure is a three-way U-Net method for accurately segmenting an uncertain boundary of a retinal blood vessel, includes: describing an uncertainty of a blood vessel boundary label, constructing an upper bound and a lower bound of the uncertain boundary based on the dilation operator and the erosion operator respectively to obtain a maximum value and a minimum value for the blood vessel boundary, and mapping the boundary with uncertain information into one range; combining an uncertainty representation of the boundary with a loss function, and designing a three-way loss function; training network parameters by adopting a stochastic gradient descent algorithm and utilizing a total loss of the three-way loss function; and designing and implements an auxiliary diagnosis application system for intelligently segmenting the retinal blood vessel with functions of the fundus data acquisition, the intelligent accurate segmentation and the auxiliary diagnosis for the retinal blood vessel.

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: Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parametrized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.

Abstract: Disclosed herein is a system and method for obtaining and generating motion capture datasets relating to various working activities for ergonomic risk assessment. An example system may comprise a computing device that obtains first data from multiple motion capture cameras, obtains second data from multiple visible light imaging sensors, calculates 3D positions of multiple reflective markers positioned on several subjects performing various working activities based on the first data, labels each marker to generate marker trajectories, performs gap filing and smoothing functions on the marker trajectories to generate global marker positions, transforms the global marker positions into a corresponding image coordinate system of each sensor to generate 3D pose data of the subjects at each sensor viewpoint, projects the 3D pose data into frames of the second data to generate 2D pose data, and generates a dataset comprising the second data, the 2D pose data, and the 3D pose data.

Abstract: A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

Abstract: A user computing entity provides a specimen score for use in selecting a sperm for use in a fertilization event. The user computing entity provides the score by obtaining specimen image data comprising imaging data of one or more specimen, the imaging data corresponds to at least one type of imaging; generating a specimen scoring request including the specimen image data; providing the specimen scoring request for receipt by a network computing entity; receiving a specimen response comprising a respective specimen score for the one or more specimen, the respective specimen score for the one or more specimen generated by a machine-learning trained specimen analysis model; processing the respective specimen score for the one or more specimen to generate a graphical representation of the respective specimen score; and causing display of the graphical representation of the respective specimen score.

Abstract: The present invention discloses a method and a system for prostate multi-modal MR image classification based on a foveated residual network, the method comprising: replacing convolution kernels of a residual network using blur kernels in a foveation operator, thereby constructing a foveated residual network; training the foveated residual network using prostate multi-modal MR images having category labels, to obtain a trained foveated residual network; and classifying, using the foveated residual network, a prostate multi-modal MR image to be classified, so as to obtain a classification result. In the present invention, a foveation operator is designed based on human visual characteristics, blur kernels of the operator are extracted and used to replace convolution kernels in a residual network, thereby constructing a foveated deep learning network which can extract features that conform to the human visual characteristics, thereby improving the classification accuracy of prostate multi-modal MR images.

Abstract: A system generates a context mask based on quantitative image data. The system obtains the quantitative image data which was captured via a quantitative imaging of a sample. The quantitative image data is compared to previous quantitative image data through application of the quantitative image data to a neural network trained using the previous quantitative image data and corresponding constructed context masks. The comparison generates the context mask for the quantitative image data. The context mask provides context for the quantitative parameter values that facilities characterization of the sample.

Abstract: A region extraction device and a method thereof in a stage previous to detection of a target object, capable of appropriately extracting a region having a possibility of presence of a target object emitting light having a light emission spectrum of a specific narrowband as a determination region in an imaging region, and an object detection apparatus and a method thereof capable of efficiently detecting the target object using a region extraction result are provided. In a region extraction method, a plurality of images including an image of a second narrowband corresponding to a first narrowband of light emitted by a target object and an image of a third narrowband different from the second narrowband are acquired from a multispectral camera.

Abstract: Systems and methods are disclosed for performing operations comprising: receiving a current fraction image and a reference image each depicting an anatomy of a subject, the reference image corresponding to a previous radiotherapy treatment fraction, the current fraction image corresponding to a current radiotherapy treatment fraction; obtaining a reference segmentation associated with the reference image; encoding, by a current fraction encoder, the current fraction image to generate a first set of features; encoding, by a reference encoder, the reference image and the reference segmentation to generate a second set of features; decoding, by a decoder, a combination of the first set of features and the second set of features to generate a predicted segmentation of the current fraction image; and configuring a radiotherapy treatment parameter based on the predicted segmentation of the current fraction image.

Abstract: A method, performed by a processor, for processing a blood vessel image from an angiography image may comprise the steps of: extracting a target blood vessel from a blood vessel image; identifying an error portion from the extraction result of the target blood vessel on the basis of at least one of blood vessel structure data related to the target blood vessel, curvature information of the target blood vessel, diameter information of the target blood vessel, and brightness information of the target blood vessel; and in response to a case where an error portion is identified in the target blood vessel, correcting the identified error portion.

Abstract: The present disclosure is generally related to systems and methods that can be implemented in a mobile application to allow users, such as parents and care providers, to measure and monitor, for example, a patient's body including an infant's head shape, at the point of care. The point of care can be, for instance, the home environment, a doctor's office, or a hospital setting. After acquiring 2D and/or 3D images of the body part, parameters reflecting potential deformity can be calculated. If abnormal measurements are determined, the user can be guided through therapeutic options to improve the condition. Based on the severity of the condition, different recommendations can be provided. Moreover, longitudinal monitoring and evaluation of the parameters can be performed. Monitoring of the normal child development can also be performed through longitudinal determination of parameters and comparison to normative values. Data can be shared with clinician's office.

Abstract: Disclosed are system, method, and computer program product embodiments for determining a digital marker indicative of a neurological condition. An embodiment operates by detecting a region of interest in an image frame of a face of a user. The region of interest includes an eye of the user and the image frame is obtained from a video stream. The embodiment then generates a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, performs deconvolution on the portion of the image frame specified by the region of interest using the PSF, determines a set of oculometric parameters of the eye of the user using the deconvolved image, and determines one or more digital markers of the user based on the set of oculometric parameters. The one or more digital markers are indicative of a neurological condition of the user.

Abstract: A method and system to generate a probabilistic prediction of the presence/absence of cancer in longitudinal and current image datasets, and/or multimodal image datasets, and the location of the cancer, is described. The method and system uses an ensemble of deep learning models. The ensemble includes a global model in the form of a 3D convolutional neural network (CNN) extracting features in the datasets indicative of the presence of cancer on a global basis. The ensemble also includes a two-stage prediction model which includes a first stage or detection model which identifies cancer detection candidates (different cropped volumes of 3D data in the a dataset containing candidates which may be cancer) and a second stage or probability model which incorporates the longitudinal datasets (or multimodal images in a multimodal dataset) and the extracted features from the global model and assigns a cancer probability p to each of the cancer detection candidates.

Abstract: A method for imaging includes acquiring surface image data for a target using a first imaging modality. A visual representation of the target based on the surface image data is generated. Internal image data for the target is acquired using a second imaging modality. During acquisition of the internal image data, the visual representation of the target based on the acquired internal image data is updated.