Abstract: The system obtains a first digital image and a hash associated with the first digital image, where the hash associated with the first digital image is a hash of analog values recorded by a sensor involved in generating the first digital image. Based on the hash, the system retrieves the analog values from a database. Based on the analog values, the system reconstructs a second digital image. The system determines whether the first digital image and the second digital image are substantially the same. Upon determining that the first digital image and the second digital image are substantially the same, the system provides a first notification that the first digital image is the same as the second digital image, otherwise the system provides a second notification that the first digital image is different from the second digital image.

Abstract: Digital pathology is a promising alternative to manual slide analysis, but improvements in imaging and analysis methods are needed to provide an analysis data set of images that can easily be handled, stored, and importantly, delivered from one data location to another. Methods and software are described herein to improve image collection and analysis.

Abstract: Methods and systems are provided for detecting focal lesions in multi-phase or multi-sequence medical imaging studies of medical images. One system includes memory for storing medical images and an electronic processor. The electronic processor is configured to: detect native candidate lesions for first phase computer tomography (CT) scans, detect candidate lesions for second phase CT scans, and project the candidate lesions detected for the first phase CT scans and/or the second phase CT scans on a same common domain. Once the candidate lesions are registered together, the electronic processor performs a matching algorithm for each of the native candidate lesions of the first phase CT scans with the registered candidate lesions from the second phase CT scans to determine presence and contours of valid lesions for the medical images, and to discard candidate lesions that are not acceptable.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: An ophthalmic imaging system comprises a first imaging subsystem, a sensor array and a second imaging subsystem. The first imaging subsystem forms a primary image of the interior of an eye through the diameter of the eye pupil. The second imaging subsystem comprises a lens array to form an array of secondary images on the sensor array. The lens array is composed of a plurality of types of lenses, each type having a different optical power.

Abstract: A system, method and non-transient computer readable medium for tracking hypoglycemia risk in patients with diabetes exercise. A system may include a digital processor configured to execute instructions to receive an input from each available data source of a plurality of intermittently available data sources; determine a plurality of probability signals for impending hypoglycemia, wherein each probability signal is based on one or more of the inputs from the available data sources or a lack of input from an unavailable data source; wherein a probability signal for each unavailable data source is assigned a value corresponding to a zone of uncertainty; and determine an aggregate risk of hypoglycemia based on the plurality of intermittently data sources by aggregating the plurality of probability signals.

Abstract: In various embodiments, a gradient modeling application automatically generates designs of three-dimensional (3D) objects. The gradient modeling application generates a set of points based on a resolution and a perimeter. The gradient modeling application computes a set of displacement values based on the set of points, a first two-dimensional (2D) border, and a first displacement parameter that is associated with the first 2D border. Based on the set of displacement values, the gradient modeling application generates a 3D object design.

Abstract: Methods for generating a deep neural net and for localizing an object in an input image, the deep neural net, a corresponding computer program product, and a corresponding computer-readable storage medium are provided. A discriminative counting model is trained to classify images according to a number of objects of a predetermined type depicted in each of the images, and a segmentation model is trained to segment images by classifying each pixel according to what image part the pixel belongs to. Parts and/or features of both models are combined to form the deep neural net. The deep neural net is adapted to generate, in a single forward pass, a map indicating locations of any objects for each input image.

Abstract: The method includes receiving digital images of tissue samples of patients, the images having assigned a label indicating a patient-related attribute value; splitting each received image into a set of image tiles; computing a feature vector for each tile; training a Multiple-Instance-Learning program on all the tiles and respective feature vectors for computing for each of the tiles a numerical value being indicative of the predictive power of the feature vector associated with the tile in respect to the label of the tile's respective image; and outputting a report gallery including tiles sorted in accordance with their respectively computed numerical value and/or including a graphical representation of the numerical value.

Abstract: During operation, a computer system may apply a pretrained predictive model to information for at least a subset of a plurality of individuals, and may determine levels of uncertainty in results of the pretrained predictive model for at least the subset of the plurality of individuals. Then, the computer system may dynamically adapt a at least one threshold range based at least in part on the determined levels of uncertainty and a predefined target performance of the pretrained predictive model for the plurality of individuals. Next, the computer system may perform different remedial actions for a first group of individuals in the plurality of individuals having the results where the levels of uncertainty are within the at least one threshold range and a second group of individuals in the plurality of individuals having the results where the levels of uncertainty are outside of the at least one threshold range.

Abstract: A method includes receiving anatomic image data comprising a plurality of graphical units; generating an initial segmented set of the plurality of graphical units associated with an anatomic tree structure; receiving an indication of a first user selected graphical unit, the first user selected graphical unit being outside the initial segmented set of the plurality of graphical units; applying a cost function to determine a first sequence of graphical units from the first user selected graphical unit to the initial segmented set of graphical units; receiving an indication of a second user selected graphical unit, the second user selected graphical unit being selected from the first sequence of the plurality of graphical units; and applying the cost function to determine a second sequence of the plurality of graphical units from the second user selected graphical unit to the initial segmented set of the plurality of graphical units.

Abstract: A system and method to analyze image data. The image data may be used to assist in determine the presence of a feature in the image. The feature may include a bubble.

Abstract: A non-transitory computer-readable storage medium storing a set of program instructions for an image processing apparatus is disclosed. The set of program instructions, when executed by a controller of the image processing apparatus, causes the image processing apparatus to: acquire input image data indicating an input image; and input the input image data to a trained machine learning model configured to perform calculation processing on the input image data and to generate output image data corresponding to the input image data. The machine learning model is trained to generate the output image data indicating a first area and a second area. The first area is an area in the input image in which it is preferable to perform first image processing. The second area is an area in the input image in which it is preferable to perform second image processing different from the first image processing.

Abstract: The present disclosure provides a computer-implemented method, a device, and a storage medium. The method includes inputting an image into an attention-enhanced high-resolution network (AHRNet) to extract feature maps for generating a first feature map; generating a first probability map which is concatenated with the first feature map to form a concatenated first feature map, and updating the AHRNet using the first segmentation loss; generating a second feature map, and scaling the second feature map to form a third feature map; generating a second probability map which is concatenated with the third feature map to form a concatenated third feature map, and updating the AHRNet using the second segmentation loss; generating a fourth feature map, and scaling the fourth feature map to form a fifth feature map; updating the AHRNet using the third segmentation loss and the regional level set loss; and outputting the third probability map.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A computer apparatus and method for identifying and visualizing tumors in a histological image and measuring a tumor margin are provided. A CNN is used to classify pixels in the image according to whether they are determined to relate to non-tumorous tissue, or one or more classes for tumorous tissue. Segmentation is carried out based on the CNN results to generate a mask that marks areas occupied by individual tumors. Summary statistics for each tumor are computed and supplied to a filter which edits the segmentation mask by filtering out tumors deemed to be insignificant. Optionally, the tumors that pass the filter may be ranked according to the summary statistics, for example in order of clinical relevance or by a sensible order of review for a pathologist. A visualization application can then display the histological image having regard to the segmentation mask, summary statistics and/or ranking. Tumor masses extracted by resection are painted with an ink to highlight its surface region.

Abstract: A method includes receiving, by an electronic device, sensor data during a spirometry test of a user, the sensor data comprising audio data of the user and distance data of a distance from a face of the user to the electronic device. The method also includes obtaining, by the electronic device, an amount of air volume exchange and at least one pulmonary health parameter that are determined based on the audio data and the distance data. The method also includes presenting an indicator on a display of the electronic device for use by the user or a medical provider, the indicator representing the amount of air volume exchange.

Abstract: Systems and methods for aiding users in viewing, assessing and analyzing images, especially images of lumens and medical devices contained within the lumens. Systems and methods for interacting with images of lumens and medical devices, for example through a graphical user interface.

Abstract: The present invention relates biometric authentication using an optical biometric arrangement comprising an image sensor comprising a photodetector pixel array configured to capture an image of an object, the image sensor being arranged under a color controllable light source comprising light source units, the method comprising: providing a light pattern comprising portions of different light intensity for illuminating the object; acquiring an image of the object, the image comprising image portions corresponding to the portions of different light intensity of the light pattern illuminating the object, at least one image portion being captured by pixels in the photodetector pixel array arranged directly under a light source being active during image acquisition, and at least one image portion being captured by pixels in the photodetector pixel array arranged under an at least partly in-active illumination area of the color controllable light source during image acquisition, and performing biometric authentica

Abstract: The present invention provides a method to consistently and continuously monitor an individual's ocular fundus via the acquisition of standardized fundus images longitudinally, especially for (but not limited to) individuals with diabetic retinopathy. Briefly, the key points of the present invention include: (a) using an aiming beam to target anatomic landmarks of the ocular fundus; (b) using normal anatomic landmarks as registration points; (c) providing a method of image acquisition to create standardized images.

Abstract: Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and/or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and/or to track treatment and/or the progress of a disease.

Abstract: A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in advance in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

Abstract: Systems, methods, and apparatus are disclosed for determining quantitative hormone and chemical analyte results from qualitative test results. An image is taken of an ovulation test device. The image is analyzed to identify a darkness intensity ratio (T/C ratio) between a darkness value of a test-line to a darkness value of a control-line. Additionally, a quantitative substance level may be determined using the T/C ratio, by identifying the type of test device and referencing a data structure that relates quantitative substance levels to T/C ratios for the identified type of test device.

Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

Abstract: Apparatus for recording information concerning the use of an injection device, the apparatus comprising: one or more sensors for providing information indicative of how the injection device is used; a processor configured to analyse said information detected by the one or more sensors to determine use information, said use information corresponding to how the injection device is used; and a memory for storing said use information in an image format such that said use information can be retrieved from the memory and displayed in an image.

Abstract: Disclosed embodiments determine, at an early stage, suitability of an intraoperative image for further intraoperative surgical analysis. The determination of suitability may be made using a first angle (such as a first obturator angle) based on at least three pelvic feature points in a preoperative image, a corresponding second angle (such as a corresponding second obturator angle) based on at least three corresponding pelvic feature points in an intraoperative image, and by comparing the first angle and the corresponding second angle to determine intraoperative image suitability. The first intra-operative image is indicated as suitable for further intraoperative analysis when an absolute value of a difference between the first angle and the corresponding second angle does not exceed a threshold. When the intraoperative image is determined as unsuitable for further intraoperative analysis, an indication of a movement direction for a fluoroscopy camera used to capture the intraoperative image is provided.

Abstract: A system and method for data filtering of passageway sensor data include traversing a plurality of passageways with a flexible elongate device and recording, during the traversing, positional data using one or more sensors coupled to the flexible elongate device. The positional data includes a first plurality of positional data points recorded during a first insertion of the flexible elongate device into a first passageway and a second plurality of positional data points recorded during a first retraction of the flexible elongate device within the first passageway. The system and method further include labeling the first plurality of positional data points and the second plurality of positional data points as first common passageway positional data points and registering the positional data with a model of the plurality of passageways, wherein the registering includes matching the first common passageway positional data points with corresponding first common passageway model data points.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: An example method for training a neural network includes generating a training data set of point clouds. The training data set includes pairs of closed surfaces point clouds and non-closed surfaces point clouds. The method further includes, for each of the closed surface point clouds and the non-closed surface point clouds, generating a two-dimensional (2D) image by rendering a three-dimensional scene. The 2D image for the non-closed surfaces point clouds includes a gap in a surface, and the 2D image for the closed surfaces point clouds are free of gaps. The method further includes training the neural network to generate a trained neural network. The method further includes filling, using the trained neural network, gaps between scan points of the 2D image, and de-noising, using the trained neural network, scan point cloud data to generate a closed surface image of the scan point cloud data.

Abstract: Systems and methods are disclosed for predicting healthy lumen radius and calculating a vessel lumen narrowing score. One method of identifying a lumen diameter of a patient's vasculature includes: receiving a data set including one or more lumen segmentations of known healthy vessel segments of a plurality of individuals; extracting one or more lumen features for each of the vessel segments; receiving a lumen segmentation of a patient's vasculature; determining a section of the patient's vasculature; and determining a healthy lumen diameter of the section of the patient's vasculature using the extracted one or more features for each of the known healthy vessel segments of the plurality of individuals.

Abstract: A method includes generating one or more nominal images of at least a portion of a digital twin of an environment, defining one or more anomalous characteristics of the one or more components, generating one or more anomalous images of the portion of the digital twin of the environment based on the field of view and the one or more anomalous characteristics, performing a tessellation routine and a texture mapping routine on the one or more nominal images and the one or more anomalous images to generate a plurality of synthetic images, and labeling, for each synthetic image from among the plurality of synthetic images, the synthetic image as one of an anomalous type, a nominal type, or a combination thereof.

Abstract: A system and a method for processing an image so as to perform instance segmentation. The system/method includes: a—inputting (S1) the image (IMG) to a first neural network configured to output an affinity graph (AF), and b—inputting (S2), to a second neural network, the affinity graph and a predefined seed-map (SM), so as to determine whether other pixels belong to a same instance, and set at a first value the value of the other pixels determined as belonging to the same instance.

Abstract: Systems, methods and apparatus are provided through which in some implementations, a system calculates a potential for ACL injury of a subject using skeletal tracking and calculating joint angles at different time points from the motion, angle of the knee joint, jumping/landing mechanics and/or balance of the subject.

Abstract: The disclosed techniques are focused on processes for encoding an enhanced image with non-image data. Notably, the “non-image data” is distinct from “image data” in that the image data defines display characteristics of an image (e.g., display properties of a pixel) while the non-image data is unrestricted and can describe any data, even data different than display characteristics. An image is accessed, where the image includes at least one pixel that is associated with at least one color channel. Non-image data is encoded into the color channel. An index, which maps where the non-image data has been encoded in the color channel of the pixel, is generated or modified. As a result of encoding the non-image data into the color channel, an enhanced image is generated.

Abstract: The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.

Abstract: A computer-implemented method of detecting and quantifying a spinal curve is disclosed herein. The method comprises obtaining an infrared radiometer camera, positioning the infrared radiometer camera for receiving thermal data for a spine of a subject, the camera being horizontally spaced about ½ meters to about 3 meters from the spine, scanning at least a portion of the spine with the infrared radiometer camera to obtain the thermal data, analyzing the thermal data using machine learning software which uses a classification algorithm to determine the presence of the spinal curve, and calculating a first Cobb angle for the curve of the subject's spine. Corresponding systems and additional methods also are disclosed.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: A high-speed stereo 3D multimodal imaging system including a beam splitter, a pair of mirrors, a tight focus lens, a microscope stage, an imaging lens, a large area array detector, and a true 3D imaging module is provided. The beam splitter is configured to generate near-infrared ultrafast pulsed laser sub-beams with different angles. The mirrors respectively reflect the beams. The tight focus lens tightly focuses the beams onto a 3D micro-nanoscale sample, which can be carried and three-dimensionally displaced by the microscope stage. The imaging lens collects signals generated due to scattering of the tightly focused beams that are irradiated onto the sample so as to obtain two 2D images. The large area array detector simultaneously detects the two 2D images so that the true 3D imaging module can perform a true 3D imaging according to the detected 2D images. A high-speed stereo 3D multimodal imaging method is also provided.

Abstract: This invention provides a system and method for detecting and acquiring one or more in-focus images of one or more barcodes within the field of view of an imaging device. A measurement process measures depth-of-field of barcode detection. A plurality of nominal coarse focus settings of a variable lens allow sampling, in steps, of a lens adjustment range corresponding to allowable distances between the one or more barcodes and the image sensor, so that a step size of the sampling is less than a fraction of the depth-of-field of barcode detection. An acquisition process acquires a nominal coarse focus image for each nominal coarse focus setting. A barcode detection process detects one or more barcode-like regions and respective likelihoods. A fine focus process fine-adjusts, for each high-likelihood barcode, the variable lens near a location of the barcode-like regions. The process acquires an image for decoding using the fine adjusted setting.

Abstract: A spine measurement system comprises an optical measurement probe, one or more targets, a fluoroscope, and a remote station. A-P and lateral images of the spine are taken using the fluoroscope and provided to the remote station. The remote station includes computer vision that can identify endplates and pedicle screws in the spine. The computer vision in the remote station is further used to identify vertebra and bone landmarks of the spine. The remote station can generate quantitative measurement data such as Cobb angles and axial rotation of the spine from the fluoroscope images that correspond to the spine deformity. The optical measurement probe can send images of the spine with pedicle screw extenders extending from the pedicle screws to the remote station. The remotes station using computer vision can provide spine metrics in real-time by tracking position of the pedicle screw extenders.

Abstract: Provided are a medical image processing apparatus, method, program, and a diagnosis support apparatus, method, and program that can efficiently collect medical images that have a large contribution to improving the accuracy of a diagnosis using medical images. The medical image processing apparatus includes a reception unit that receives an input of a medical image and patient information corresponding to the medical image, an analysis result acquisition unit that acquires an analysis result obtained by analyzing the medical image, a detection unit that detects whether or not the analysis result has been corrected, and a data processing unit that creates and stores data in which identification information capable of identifying a patient is concealed in a case where it is detected that the analysis result has been corrected.

Abstract: A system for identifying melanoma and other skin cancer in a dermoscopy image comprises: an image analyzer having at least one processor that instantiates at least one component stored in a memory, the at least one component comprising: a segmenter configured to segment a lesion from the rest of the image, a handcrafted feature component including: a median color splitting model for separating the image into a plurality of color regions, a vessel detection model for detecting elevated vascularity, an atypical pigment network detection model for identifying a pigment network whose structure varies in size and shape, a salient point detection model for detecting salient points based on an intensity plane of the image, a color detection model for detecting at least one of a white area, a pink shade, a pink blush, and a semi-translucency, a hair detection model for characterizing detected hairs and ruler marks, an outside model that finds the above model features on non-dark-corner areas outside the segmented are

Abstract: The disclosure provides a transmissive light based tremor identification method and a system thereof. The method includes: projecting, with a transmissive light, a first optical pattern to a part to be measured, wherein the transmissive light penetrates a surface of the part to be measured and correspondingly forms a second optical pattern on an internal structure of the part to be measured, and the second optical pattern is synthesized to include at least one intersection; capturing a plurality of images of the second optical pattern on the internal structure of the part to be measured and acquiring a motion feature of each intersection based on the images; and identifying a tremor pattern of the internal structure of the part to be measured based on the motion feature of each intersection.

Abstract: The present invention provides a self-location estimation method including: a first step of estimating the self-location of a moving body (1) from the detection information of a plurality of sensors (5) to (8) by using a plurality of algorithms (11) to (13); a second step of determining a weighting factor for each algorithm from one or more state quantities A, B and C, which are obtained by estimation processing for each of a plurality of algorithms, by using a trained neural network (14); and a third step of identifying, as the self-location of the moving body (1), a location obtained by synthesizing the self-locations, which have been estimated by the algorithms, by using weighting factors.

Abstract: A method and a device for self-supervised learning, a storage medium, and an electronic device are provided. The method includes: organizing real points in one column along a vertical direction into a pillar; determining a predicted point in a next frame; determining a first loss term based on a minimum distance among distances between predicted points in the next frame and real points in the next frame, and generating a loss function including the first loss term; and performing self-supervised learning processing based on the loss function. A pillar motion parameter representing motion of a real point is determined with the pillar as a unit, so as to enhance correlation between point clouds. Self-supervised learning can be realized in a case of no precise correspondence between the predicted point and the real point, and training is performed based on a large number of unlabeled point clouds.

Abstract: A wearable device for high-density-diffuse optical tomography includes a plurality of source modules that include source housings containing a light source. The device also includes a plurality of detector modules that include detector housings, a photodiodes, transimpedance amplifiers operatively coupled to the photodiodes, and analog to digital converters operatively coupled to the transimpedance amplifier. Each detector housing contains at least one of the photodiode, the analog to digital converter, and the transimpedance amplifier. Each source and detector housing is configurable to contact a subject's scalp and deliver or receive light from the scalp at one position. The wearable device also includes a control circuit operatively coupled to the plurality of source modules and to the plurality of detector modules to operate the source and detector modules in a coordinated manner to acquire a plurality of diffuse optical tomography measurements.

Abstract: A microscope system comprising an eyepiece, an objective that guides light from a sample to the eyepiece, a tube lens that is disposed on a light path between the eyepiece and the objective and forms an optical image of the sample on the basis of light therefrom, a projection apparatus that projects a projection image including a first assistance image onto an image plane on which the optical image is formed, and a processor that performs processes. The processes include generating projection image data representing the projection image. The first assistance image is an image of the sample in which a region wider than an actual field of view corresponding to the optical image is seen, The first assistance image is projected onto a portion of the image plane that is close to an outer edge of the optical image.

Abstract: An information processing system includes a storage device configured to store therein information of a trained model, and a processor. The processor performs a detection process based on the information of the trained model, and outputs position information detected in the detection process, the detection process being a process that detects the position information about an object from a detection image. The trained model is trained based on training data in which an object in a training image is added with multiple annotations, and is trained to output position information about the object in the training image based on an overlapping relationship of the multiple annotations.

Abstract: Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.