Abstract: In one embodiment, an automatic high-speed X-ray system may generate a high-resolution X-ray image of an inspected sample at a direction substantially orthogonal to a plane of the inspected sample. The system may determine a first cross-sectional shape of a first portion of a first element of interest in the inspected sample based on grayscale values of the X-ray image associated with the first element of interest. The system may determine a second cross-sectional shape of a second portion of the first element of interest in the inspected sample. The second cross-sectional shape may be determined based on the grayscale values of the X-ray image associated with the first element of interest. The system may determine one or more first metrological parameters associated with the first element of interest in the inspected sample based a comparison of the first cross-sectional shape and the second cross-sectional shape.

Abstract: The present invention discloses a capsule endoscope system, a method of identifying stained area in endoscopic images, and a computer readable storage medium. The method comprises: obtaining a basic image taken by a photographing device; removing abnormal pixels from the basic image and recording it as a processed image; obtaining the hue, saturation and value of each pixel in the processed image of HSV format; setting a first condition that the hue is in a D1 range, the saturation is in a D2 range, and the value is in a D3 range; obtaining the b component of each pixel in the processed image of Lab format; setting a second condition that the b component is in a D4 range; collecting and processing the pixels that meet the first condition and/or the second condition to form a set C; calculating the initial range area S1 of the set C.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: A system includes a first device with a primary screening area; a second device with a secondary screening area different from the primary screening area; and processor circuitry. The first device includes a first antenna and a first communication device; the second device includes a second antenna and a second communication device. The first antenna irradiates an electromagnetic wave to a target in the primary screening area and receives an electromagnetic wave reflected by the target; the second antenna irradiates an electromagnetic wave to the target in the secondary screening area and receives an electromagnetic wave reflected by the target; and the processor circuitry determines a possibility that the target possesses a predetermined article, based on a level of the electromagnetic wave received by the first antenna, and determines that screening by the second device is required for the target in accordance with the possibility.

Abstract: The disclosed embodiments provide a system that images a tissue sample. During operation, the system receives the tissue sample, which has been stained using absorbing and fluorescently emitting stains. Next, the system illuminates the tissue sample with excitation light having a wavelength or wavelengths in a range that covers a portion of an absorption spectrum for both fluorescently emitting and absorbing stains, whereby the excitation light interacts with stained tissue located inside the tissue sample to both limit penetration depth and generate emitted dye fluorescence and tissue autofluorescence that provides a backlight, which is absorbed by features in stained tissue located on or near the surface of the tissue sample. Next, the system uses an imaging device to capture an image of emitted fluorescence that emanates from the surface of the tissue sample.

Abstract: According to some embodiments, a system for transmitting and rendering a visualization of at least one object is described herein. The system includes a web server having access to at least one web page that includes three-dimensional embedding (“3D embedding”) instructions for requesting at least one 3D visualization associated with an object; a client computer in the data communication with the at least one web server, the client computer configured to receive the at least one web page and execute the 3D-embedding instructions included therein to send a request for the visualization associated with the object; and a visualization server in data communication with the client computer configured to determine whether the visualization is available, and determine whether the client computer is operable to render the 3D visualization, and determine whether to provide the 3D visualization data to the client computer.

Abstract: Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: The methods and systems described herein enable the accurate diagnosis of novel biotypes of depression that transcend current diagnostic boundaries and may be useful for identifying individuals who are most likely to benefit from antidepressant treatment. Functional magnetic resonance imaging is used to characterize the architecture of functional connectivity across the brain to show that patients with depression can be subdivided into four neurophysiological biotypes based solely on unique patterns of abnormal connectivity in resting state brain networks. Clustering subjects on this basis reduces diagnostic heterogeneity, enabling the development of depression biotype classifiers for diagnosing biotypes of depresion in individual patients, These biotypes also predict differing responses to antidepressant treatment, and abnormal connectivity patterns can be used to track changes in depression severity over time.

Abstract: Methods and systems are described that receive intraoral scan data in response to an optical scan of a surface of the tooth and a material disposed between the tooth and a gingiva surrounding the tooth, the material separating the surrounding gingiva from the tooth and covering a sub-gingival surface of the tooth. The received intraoral scan data is processed to differentiate the first optical scan data associated with the sub-gingival surface of the tooth and the second optical scan data associated with the material covering the sub-gingival surface of the tooth. The three-dimensional model of the tooth is generated based on the first optical scan data that is associated with the sub-gingival surface of the tooth and the third optical scan data associated with the tooth surface that is not covered by the material such that the three-dimensional model of the tooth includes the sub-gingival surface of the tooth.

Abstract: Systems, devices, and methods relate to generation of augmented images using virtual content that is part of an augmented reality presentation and images of a scene captured via an image sensor. A wearable heads-up display (WHUD) may present an augmented reality presentation with virtual content projected into a field of view of a scene, while an image sensor captures images of the scene. The image sensor may be part of the WHUD or part of a separate device, for instance part of a smartphone. A wearer may view a scene via the WHUD and capture an image of all or a portion of the scene, for instance via a camera of either the WHUD or a separate device. An application monitors the virtual content and can generate an augmented image, which can be transmitted/printed, replicating the augmented reality experience and/or adding customized messages to the resulting augmented image.

Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

Abstract: A computer-based method for quantitative evaluation of computed tomography (CT) images of the head, particularly in circumstances of neurological emergency such as acute intracranial hemorrhage, evidence of intracranial mass effect, and acute stroke. The method comprises: calculation of volumes of abnormal areas such as locations of hemorrhage; quantification of severity of midline shift and basilar cistern effacement; and rapid identification of anatomical locations of abnormal findings. The methods comprise use of heuristics, convolutional neural networks, deep learning, edge detection, and Hough transform.

Abstract: The present invention has been made in view of the foregoing, and an object thereof is to provide an information processing device capable of appropriately determining a necessity of the ultrasonography even when a high-density mammary gland area exists in a narrow area. The present invention provides an information processing device comprising: a mammary gland area extractor configured to extract a mammary gland area in a mammography image; a mammary gland pixel detector configured to detect a mammary gland pixel in the mammary gland area; and a mammary gland density calculator configured to calculate a mammary gland density based on a ratio of the mammary gland pixels to the mammary gland area, wherein the mammary gland area is a narrower area than an entire breast in the mammography image.

Abstract: An image generator generates images of a set of virtual fibers and effects thereon by processing representations of the set of fibers and computing representation of a virtual surface for a fiber clump in the set of virtual fibers from an artist parameter representing a desired artist effect, computing correlations of the vertices from a set of vertices based on associations of the vertices corresponding to the artist parameter, computing a set of relevant vertices using the correlations of the vertices, computing orthogonal gradients to produce a plurality of gradients using a selected vertex and the set of relevant vertices for the fiber clump, and computing the virtual surface of the fiber clump from the plurality of gradients.

Abstract: A radiographic imaging apparatus includes an image processor configured to perform image processing on a radiation image generated by an image generator, and a display configured to display a processed image that has been subjected to the image processing in the image processor. The image processor is configured to perform control to create the processed image showing an expanded portion in which a balloon introduced into a subject is expanded.

Abstract: An X-ray diagnostic apparatus according to an embodiment includes an X-ray tube, an X-ray detector, image generating circuitry, and processing circuitry. The X-ray tube generates an X-ray. The X-ray detector is positioned in a manner facing the X-ray tube, and detects the X-ray. The image generating circuitry generates an X-ray image one after another, based on an output of the X-ray detector. The processing circuitry identifies an inserted object inside a subject in the X-ray image, by performing an image recognition process to the X-ray image. The processing circuitry sets the area to be searched for a marker, based on the inserted object identified in the X-ray image. The processing circuitry detects the marker in the X-ray image, by searching the area to be searched for the marker.

Abstract: Disclosed is an image judgment device including a hardware processor that functions as an image acquirer that acquires image data of a medical image that is obtained by radiographing of a part including a region to be diagnosed of a patient, a segmentation unit that extracts an anatomical structure from the medical image to generate an extracted image, an image converter that performs image conversion on the extracted image to generate a converted image; a feature value extractor that calculates a feature value from the extracted image and/or the converted image by using a result of machine learning; and a judgment unit that judges whether the medical image is taken with positioning appropriate for diagnosis based on the feature value by using a result of machine learning concerning feature values.

Abstract: Quantitative susceptibility mapping methods, systems and computer-accessible medium include generating images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach. The tissue magnetism images is then used to monitor remyelination, such as remyelination in multiple sclerosis patients in response to therapy. Multiple sclerosis lesions defined on magnetic resonance imaging are further characterized on tissue magnetism images into hyperintense, isointense and hypointense parts for measuring remyelination. Thus, magnetic susceptibility information and other tissue properties associated with at least one structure are determined.

Abstract: Systems and methods for automated stereology are provided. A method can include providing an imager for capturing a Z-stack of images of a three-dimensional (3D) object; constructing extended depth of field (EDF) images from the Z-stack of images; performing a segmentation method on the EDF images including estimating a Gaussian Mixture Model (GMM), performing morphological operations, performing watershed segmentation, constructing Voronoi diagrams and performing boundary smoothing; and determining one or more stereology parameters such as number of cells in a region.

Abstract: Certain aspects relate to systems and usage techniques for modular lateral flow assay reader devices that can receive a number of different modules having a barcode scanning input device and optional network connectivity capabilities. A barcode scanning module can provide a simple input method that reduces errors compared to manual data entry. A network connectivity module can enable transmission of test results over a public network for standardizing, tracking and electronically connecting test results from assay reader devices located throughout a network. Such devices can programmatically implement a simplified workflow whereby pressing a single button readies the device for imaging, analyzing, and data storage/transmission and, in some implementations, configures the device to operate in one of a plurality of device operation modes.

Abstract: A method for removing fringe noise in an image includes: acquiring an original image; acquiring an original frequency spectrum of one-dimensional signal of the original image; determining a noise frequency band in the original frequency spectrum, and the noise frequency band is a frequency band including a central frequency of the fringe noise; denoising the noise frequency band to obtain a denoised frequency spectrum, wherein a denoising intensity used in the denoising is gradually increased from a start frequency position of the noise frequency band, and the denoising intensity is gradually reduced from the central frequency until a stop frequency position of the noise frequency band; and generating a denoised image according to the denoised frequency spectrum.

Abstract: Apparatus and methods are described for analyzing a bodily sample. One or more microscope images of the bodily sample are acquired. Using at least one computer processor at least one sample-informative feature that is indicative of a characteristic of the bodily sample is extracted from the images. Based upon the sample-informative feature, the computer processor determines that there is a defect associated with the bodily sample, and determines a source of the defect. Other applications are also described.

Abstract: This application relates generally to automated systems and associated methods for identifying hematological abnormalities. An automated system can include at least one processor that, in operation, is configured to: receive, from a flow cytometer, a flow cytometry data matrix characterizing a tube that is associated with a sample; convert the flow cytometry data matrix into a high dimensional vector; produce a single sample high dimensional vector including a concatenation of multiple high dimensional vectors associated with the sample, wherein the multiple high dimensional vectors comprise the tube high dimensional vector; assemble a training data set including multiple sample high dimensional vectors; receive, from a datastore, outcome information including respective labels associated with each of the multiple sample high dimensional vectors; and train a classifier based on the training data set and the outcome information.

Abstract: Methods and systems are directed to enabling CNN operation close to an image sensor and facilitating CNN inference near the image sensor. The system includes, in part, a digital image sensor and a plurality of processing layers that include, in part, an attention-based preprocessing layer (APL), an inference computation layer (ICL), and a fully connected layer. The digital image sensor can include, in part, a plurality of regions and each region can be processed by region processing units disposed in the APL/ICL in parallel. Each region processing unit disposed in the APL can include, in part, an attention module that determines whether the corresponding region is a relevant region. The region processing units disposed in the APL transmit only relevant data to corresponding region processing units in the ICL and only activate the corresponding region processing units in the ICL for relevant regions. The ICL can include, in part, two convolution layers.

Abstract: Systems and methods for radiology image classification from noisy images in accordance with embodiments of the invention are illustrated. One embodiment includes noisy image classification device, including a processor, camera circuitry, and a memory containing a noisy image classification application, where the noisy image classification application directs the processor to obtain image data describing a first image taken of a second image using the camera circuitry, where the second image was produced by a medical imaging device, and where the first image is a noisy version of the second image, classify the image data using a neural network trained to be robust to noise, generate an investigation recommendation based on the classification, and provide the investigation recommendation via a display.

Abstract: Anatomical, physiological, instrumental, and other related biases are removed from functional magnetic resonance imaging (“fMRI”) signal data using deep learning algorithms and/or models, such as a neural network. Bias characterization data are used as an auxiliary input to the neural network. The bias characterization data can be subject-specific bias characterization data (e.g., cortical thickness maps, cortical orientation angle maps, vasculature maps), hardware-specific bias characterization data (e.g., coil sensitivity maps, coil transmission profiles), or both. The subject-specific bias characterization data can be extracted from the fMRI signal data using a second neural network. The bias-reduced fMRI signal data can include time-series signals, functional activation maps, functional connectivity maps, or combinations thereof.

Abstract: Disclosed are systems, devices, and methods for navigating a tool inside a luminal network. An exemplary method includes receiving image data of a patient's chest, identifying the patient's lungs, determining locations of a luminal network in the patient's lungs, identifying a target location in the patient's lungs, generating a pathway to the target location, generating a three-dimensional (3D) model of the patient's lungs, the 3D model showing the luminal network in the patient's lungs and the pathway to the target location, determining a location of a tool based on an electromagnetic (EM) sensor included in the tool as the tool is navigated within the patient's chest, displaying a view of the 3D model showing the determined location of the tool, receiving cone beam computed tomography (CBCT) image data of the patient's chest, updating the 3D model based on the CBCT image data, and displaying a view of the updated 3D model.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: determining a weighting parameter (13) of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device (12) for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (?) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Abstract: One or more embodiments of a thumbnail caching system dynamically provide a thumbnail cache of digital content items (e.g., photos, videos, audio) to a user on a client device. In particular, the thumbnail caching system provides a thumbnail cache of a digital content collection to a client device such that the thumbnail cache does not exceed a threshold storage limit for the client device. In addition, the thumbnail caching system intelligently adjusts the thumbnails within the thumbnail cache to keep the size of the thumbnail cache within the threshold storage limit irrespective of the number of digital content items stored or added to the digital content collection. Further, the thumbnail caching system can dynamically adjust the size of the thumbnail cache in response to a user adding or removing external data to the client device.

Abstract: A super-resolution reconstruction preprocessing method of contrast-enhanced ultrasound images includes: acquiring an image set to be preprocessed; acquiring grayscale fluctuation signal of a pixel point in the registered contrast-enhanced ultrasound images to be preprocessed; performing denoise reconstruction operation on the image set to be preprocessed to obtain a reconstructed feature parameter image, and performing interpolation calculation on the reconstructed feature parameter image to obtain a sparse microbubble image. By analyzing the grayscale fluctuation signals of the collocated pixel point set in the plurality of frames of the registered contrast-enhanced ultrasound images to be preprocessed, a signal-to-noise ratio and a signal-to-background ratio are improved.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: According to one embodiment, an ultrasonic diagnostic apparatus includes an ultrasonic probe and processing circuitry. The processing circuitry receives a reflected wave signal of an ultrasonic wave via the ultrasonic probe. The processing circuitry generates a reception signal based on the received reflected wave signal. The processing circuitry calculates blood flow information based on the reception signal. The processing circuitry detects a magnitude of blood flow velocity change based on the blood flow information calculated at a plurality of points in time. The processing circuitry performs smoothing processing on the blood flow information in time direction at an intensity determined in accordance with the detected magnitude of blood flow velocity change.

Abstract: The present disclosure describes methods and apparatuses for reducing metal artifacts in cone beam computed tomography (CBCT) reconstructions. The methods use multiple imaging modalities to identify and locate metal present in a region of dental patients mouth and to generate a reconstructed 3-D volume image of the region with reduced metal artifacts using data obtained by the multiple modalities. According to example embodiments, the methods include creating a metal map using data from a first imaging modality and an initial 3-D reconstruction from data obtained from a second imaging modality including CBCT imaging. The metal map is registered to the initial 3-D reconstruction with a reconstructed metal map and projection metal maps being subsequently produced and applied to projections from the CBCT imaging to generate interpolated projections.

Abstract: Disclosed are an image encoding/decoding method and apparatus. The method and apparatus select at least one reference pixel line from among multiple reference pixel lines and derive a predicted value of a pixel within a current block by using the value of at least one pixel within the selected reference pixel line(s). Alternatively, the method and apparatus derive an intra prediction mode of a reconstructed pixel region on the basis of a reference pixel region of at least one reconstructed pixel region, derive an intra prediction mode of a current block on the basis of the derived intra prediction mode of the reconstructed pixel region, obtain an intra prediction block of the current block by using the derived intra prediction mode, and reconstruct the current block by summing the obtained intra prediction block and a residual block of the current block.

Abstract: Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

Abstract: A method and device for restoring and reconstructing a light source spectrum based on a hyperspectral image are provided. A hyperspectral image is processed to obtain a first maximum spectrum. The first maximum spectrum is matched with a light source spectral basis vector set in a light source spectrum dictionary, and a linear decomposition is performed to obtain a dictionary set. The maximum value of each waveband is obtained and iterative approximation is performed. Therefore, it can quickly restore the spectrum of the light source of the shooting environment from a single hyperspectral image using a relatively simple calculation process, and can still achieve a good restoration effect for monochromatic image scenes or image scenes with few colors, even close to the real light source spectrum. After the light source spectrum is obtained, this information can be further utilized for different kinds of applications.

Abstract: The present invention relates to a diagnosis assistance system for assisting diagnosis for a plurality of diseases based on a fundus image, the diagnosis assistance system including: a fundus image obtaining unit configured to obtain a fundus image; a first processing unit configured to, for the fundus image, obtain a first result related to a first finding of a patient using a first neural network model, a second processing unit configured to, for the fundus image, obtain a second result related to a second finding of the patient using a second neural network model, a third processing unit configured to determine, on the basis of the first result and the second result, diagnostic information on the patient, and a diagnostic information output unit configured to provide the determined diagnostic information to a user.

Abstract: Eyewear having a sensor configured to sense electrical signals generated from user muscle movements to determine user facial expressions. The facial expressions are processed to provide a user input to the eyewear, to take an action such as taking an image using a camera, and to determine a user biometric such as by performing an electrocardiogram (ECG or EKG). In an example, a user can raise an eyebrow to instruct the eyewear to take an image, and squint an eye to lighten/darken a shade of an optical element.

Abstract: To generate a prediction image in which an outline of an object is clear and existence of the object is easily recognized, the image prediction system that generates a future prediction image and includes a gaze unit, a working memory unit, a control unit, and a generation model unit. The gaze unit controls a region including an object included in an observation image as a first gaze region. The working memory unit controls the first gaze region as a second gaze region when a difference in the first gaze region between the observation image and a prediction image is equal to or less than a predetermined value. The generation model unit generates prediction images of the first gaze region and the second gaze region. The control unit integrates the prediction image of the first gaze region and the prediction image of the second gaze region to generate a prediction image.

Abstract: The disclosure relates to a system and method for reconstructing a PET image. The method may include: obtaining PET data relating to an object collected by a plurality of detector units; determining functional status of the plurality of detector units; generating reconstruction data based on the functional status of the respective detector units and the PET data; and reconstructing a PET image based on the reconstruction data.

Abstract: In a photoacoustic image generation apparatus including a photoacoustic image generation unit that generates a photoacoustic image based on a detection signal and a sound speed in a subject and a sound speed setting unit that sets the sound speed in the subject, the sound speed setting unit extracts a region having a predetermined range including a high signal value pixel collection portion having a region size larger than a reference region size as a tip region in a photoacoustic image generated based on an assumed sound speed and the detection signal and sets a sound speed of a photoacoustic image having a maximum image evaluation value as the sound speed in the subject among photoacoustic images for respective sound speeds generated by changing the sound speed in a predetermined sound speed range for the tip region.

Abstract: A computer-implemented method for determining a pathology in 3D image data is describe wherein the method may comprise:receiving at least a first 3D image of a body part, the 3D image comprising voxels associated with a predetermined image volume; a first 3D convolutional neural network determining a position of a volume of interest (VOI) in the image volume of the first 3D image, the VOI being associated with a pathology of the body part, the VOI defining a sub-volume of the image volume; determining first VOI voxels by selecting voxels of the first 3D image that have a position within the VOI as determined by the first 3D convolution neural network and providing the first VOI voxels to the input of a second 3D convolutional neural network; the second 3D convolutional neural network, determining a target label value on the basis of at least the first VOI voxels, the target label value being indicative of the presence or absence of a pathology in the VOI; and, generating a medical report by associating the ta

Abstract: Methods and systems are provided for diagnosing and generating a treatment plan for temporomandibular joint dysfunction where a polymeric shell appliance is utilized to generate one or more activation forces that facilitate tooth movement. The polymeric shell appliances may comprise one or more tooth receiving cavities, in which each of the plurality of tooth receiving cavities is shaped and arranged to provide a counter moment of each of the plurality of teeth.

Abstract: The present solution can segment tracts by performing two-pass tractography. The system can first perform deterministic tractography and then probabilistic tractography. The system can use the result from the deterministic tractography to update and refine initial identified regions of interest. The refined regions of interest can be used to filter and select streamlines identified through the probabilistic tractography.

Abstract: The disclosure provides a method of and an imaging system for clinical sign detection. The method uses an imaging system having an RGB image sensor and the processing device disclosed herein. An image of a patient or examinee is captured by the RGB image sensor to generate an RGB image. Clinical signs of the patient or examinee are detected by the processing device based on the RGB images.

Abstract: Methods for using registered real-time images and prior-time anatomic images during an image-guided procedure are provided herein. An exemplary method includes obtaining a three-dimensional image of a patient anatomy and a portion of a medical instrument disposed therein. The three-dimensional image includes image information characterizing a shape of the portion of the medical instrument. A processing device segments the portion of the medical instrument from the three-dimensional image. Shape data is obtained from the portion of the medical instrument while the portion is positioned within the patient anatomy, and the processing device registers the segmented shape of the portion of the medical instrument with the shape data from the portion of the medical instrument.

Abstract: A personal care system having a treatment device for applying a treatment to the skin or hair of a user is provided. The treatment device has a camera for taking an image of the skin. The system may include or be in communication with an application programming interface (API) that can process the image. The system or an associated API can recommend the use of treatment regimens or topical products according to information determined from the digital image of the skin.

Abstract: In one example in accordance with the present disclosure, a system is described. The system includes a replicator to receive a reference model of an object to be scanned. A capture system simulator of the system simulates capture settings. The capture system includes a number of capture devices placed around the object. The capture system also includes a controller. The controller extracts simulated capture data for the capture system and determines adjustments to the capture settings based on extracted simulated capture data.

Abstract: Systems and methods use a three-dimensional snapshot of an infant, child, adolescent, or adult where the subject may have bent legs and an unaligned head position, to provide an estimate of human length. The system identifies anatomical features from the digital information and generates a virtual skeleton. The cumulative distances between pseudo-joints of the virtual skeleton provide an estimate of human length. Comparing length estimates from multiple three-dimensional snapshots of the same individual acquired over time provide an indication of growth of the infant, child, or adolescent. Daily estimates of length can detect growth faltering sooner than less frequent estimates of length, which can lead to timely intervention.