Abstract: Respective longitudinal insertion paths for two tools are planned and associated with 3D image data of a skeletal portion. While respective portions of the tools are disposed at first respective locations along their respective longitudinal insertion paths, first and second x-rays of the respective portions of the tools and the skeletal portion are acquired from respective first and second views. A computer processor matches between a tool in the first x-ray and the same tool in the second x-ray by: (A) identifying respective tool elements of each tool within the first and second x-rays, (B) registering the first and second x-rays to the 3D image data, and (C) based upon the identified respective tool elements and the registration, identifying for at least one tool element a correspondence between the tool element and the respective planned longitudinal insertion path for that tool. Other applications are also described.

Abstract: A method for generating a machine learning model for characterizing a plurality of Regions Of Interest ROIs based on a plurality of 3D medical images and an associated method for characterizing a Region Of Interest ROI based on at least one 3D medical image. The methods proposed here aim to provide complementary strategies to enable a classification of ROIs from 3D medical images which could take profit of the advantageous and complementarity of both 2D and 3D CNNs to improve the accuracy of the prediction. More precisely, the present disclosure proposes a 2D model that complements the 3D model so that the sensitivity/specificity of the diagnosis is improved by taking advantage of complementary notions.

Abstract: In a system and method for performing MR image reconstruction based on acquired MR measurement data of an organ structure of a patient, the MR measurement data of the organ structure is received, a-priori information about the organ structure from which the MR measurement data have been acquired is received, MR image reconstruction is performed based on the MR measurement data and taking into account the a-priori information, and the reconstructed MR image data is provided.

Abstract: A method and apparatus for anonymizing a three-dimensional medical image are provided. The apparatus determines a skin region of a three-dimensional medical image, generates a human mask based on a human tissue region of the three-dimensional medical image, the human tissue region including various organs, generates a skin expansion region in which the skin region of the three-dimensional medical image is expanded, generates an anonymization region obtained by removing a region corresponding to the human mask from the skin expansion region, and changes brightness values of voxels corresponding to the anonymization region in the three-dimensional medical image to a predefined value or an arbitrary value.

Abstract: A method for gating in tomographic imaging system includes steps of: (a) performing a tomographic imaging on an object for acquiring a plurality of projection images at different projection angles, wherein a target of the object moves periodically; (b) obtaining a projected position of the target on each of the projection images, wherein the projected position is a center of a target zone on each of the projection images; (c) calculating a parameter value of pixel values in the target zone on each of the projection images, and obtaining a curve of a moving cycle of the target according to the parameter values of the projection images; and (d) selecting the projection images under the same state in the moving cycle for image reconstruction according to the curve of the moving cycle of the target.

Abstract: A method for imaging may include directing a PET scanner to perform scans of a subject injected with a tracer during a first time period. The method may also include generating PET images by reconstructing the PET data that are generated by the PET scanner based on the scans of the subject. The method may also include determining a first portion of a blood input function of the tracer in the subject based on the PET images. The method may also include determining an integral of a second portion of the blood input function based on a kinetic model, the PET data, and the first portion of the blood input function. The method may also include determining kinetic parameters of the kinetic model based on the PET data, the first portion of the blood input function, and the integral of the second portion of the blood input function.

Abstract: The invention relates to a method of determining the sagittal rotation of a patient's pelvis based on a standard anterior posterior X-ray-image with known image parameters and a calibration of the image, for example by using at least one King-Mark calibration object. The angle of the pelvic rotation is determined between a pelvic plane which is orthogonal to the midsagittal plane of the pelvis, and the image plane of the X-ray-image. Assuming the patient's position shown on the X-ray-image represents a standard neutral position, the X-ray-image plane can be used as a functional reference plane for further calculations, for example during hip-replacement surgery. The present invention further relates to a corresponding computer program and system.

Abstract: Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.

Abstract: A method, performed by a computer, for volumetrically determining a mid-sagittal plane (MSP) from a plurality of magnetic resonance (MR) images of a brain is disclosed. The plurality of MR images is received and converted into a 3D volume image defined in a 3D coordinate space. Semantic segmentation of an anterior commissure (AC) region, a posterior commissure (PC) region, and a third ventricle region in the 3D volume image is performed based on a pre-trained segmentation model to generate a 3D mask image labeled with the AC region, the PC region, and the third ventricle region. Based on the labeled 3D mask image, a 3D volumetric image of the segmented third ventricle region is constructed. The MSP is determined based on the 3D volumetric image of the third ventricle region.

Abstract: Systems and methods for image processing are provided in the present disclosure. The systems may generate a preliminary image by filtering image data generated by an image acquisition device. The system may generate an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image. The first objective function may include a first term associated with a first difference between the intermediate image and the preliminary image, a second term associated with continuity of the intermediate image and a third term associated with sparsity of the intermediate image. The systems may also generate a target image by performing, based on a second objective function, a second iterative operation on the intermediate image. The second objective function may be associated with a system matrix of the image acquisition device and a second difference between the intermediate image and the target image.

Abstract: The present disclosure relates to systems and methods for medical imaging. The method may include obtain scanning data and at least one prior image of a subject. The method may include determining a restriction factor for each of the at least one prior image based on the scanning data. The restriction factor of the each prior image may relate to a motion of the subject corresponding to the scanning data. The method may include determining an objective function based on the restriction factor. The method may also include reconstructing, using the objective function, a target image of the subject based on the scanning data and the at least one prior image.

Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: Method to design manufacture an intraocular lens, comprising an optical part and haptics part, in which the optical part comprises an anterior surface with negative refractive power and a posterior surface with positive refractive power, in which in order to determine the refractive power D of the anterior surface of the optical part and the refractive power D? of the posterior surface of the optical part the following steps are performed: a plurality of light rays are provided over the normal eye, both on the optical axis (1001) and forming different angles (714, 715, 716) with respect to the optical axis (1001), for each light ray with its angle the axial length of the eye and the refractive power of the cornea are measured, determination of the shape of the retina (200) through a mathematical fit to an aspherical surface that contains the measured points, calculation of the arc length S that goes from the intersection of the optical axis with the retina (200) of the eye to the intersection of the light

Abstract: The ratio of arterial spin labeled (ASL) perfusion to diffusion weighted imaging (DWI) is generally homogeneous in the anoxic/hypoxic injury population. Conversely, the ratio is more heterogeneous in the non-anoxic/hypoxic population. By plotting these ratios in a graphical format in the form of an axial color map of the brain—referred to as a normalized diffusion to perfusion (NDP) ratio colormap—it may be determined whether a patient has suffered from an anoxic/hypoxic injury. Thus, the anoxic and non-anoxic injury patients will have, respectively, homogenous and heterogeneous color maps. Anoxic brain injury patients have a global homogeneously positive relationship between qualitative ASL perfusion and diffusion weighted signal such that areas of restricted diffusion show significantly increased ASL perfusion signal, which may be attributable to BBB integrity.

Abstract: A method for scanning, identifying, and navigating at least one anatomical object of a patient via an imaging system. Such system includes scanning the anatomical object via a probe of the imaging system, identifying the anatomical object via the probe, and navigating the anatomical object via the probe. Further, the method includes collecting data, inputting the collected data into a deep learning network configured to learn the scanning, identifying, and navigating steps. In addition, the method includes generating a probe visualization guide for an operator based on the deep learning network. Thus, the method also includes displaying the probe visualization guide to the operator wherein the probe visualization guide instructs the operator how to maneuver the probe so as to locate the anatomical object. In addition, the method also includes using haptic feedback in the probe to guide the operator to the anatomical object of the patient.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: An image processing apparatus includes a special light image acquisition unit that acquires a special light image having information in a specific wavelength band, a generation unit that generates depth information at a predetermined position in a living body using the special light image, and a detection unit that detects a predetermined region using the depth information. The image processing apparatus further includes a normal light image acquisition unit that acquires a normal light image having information in a wavelength band of white light. The specific wavelength band is, for example, infrared light. The generation unit calculates a difference in a depth direction between the special light image and the normal light image to generate depth information at the predetermined position. The detection unit detects a position in which the depth information is a predetermined threshold or more as a bleeding point. The present technology is applicable to an endoscope.

Abstract: A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels.

Abstract: A medical imaging system having at least one medical imaging device providing image data of a subject is provided. The medical imaging system further includes a processing system programmed to train a deep learning (DL) network using a plurality of training images to predict noise in input data. The plurality of training images includes a plurality of excitation (NEX) images acquired for each line of k-space training data. The processing system is further programmed to use the trained DL network to determine noise in the image data of the subject and to generate a denoised medical image of the subject having reduced noise based on the determined noise in the image data.

Abstract: A system for imaging reconstruction is provided. The system may obtain a first set of image data of a subject acquired by a scanner and a second set of image data of the subject acquired by the scanner. The first set of image data may correspond to a first angle range of the scanner. The second set of image data may correspond to a second angle range of the scanner. The first angle range may be different from the second angle range. The system may also generate a first image based on the first set of image data and generate a second image based on the second set of image data. The system may further generate a target image based on the first image and the second image.

Abstract: The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.

Abstract: Computer-implemented method for determining nuclear medical image data sets in dynamic nuclear medical imaging. The method includes using a trained function to determine at least one further nuclear medical image data set for at least one frame if a basis raw data set is taken from one single frame, wherein input data of the trained function includes at least one of the nuclear medical raw data set of the respective frame or a preliminary reconstructed image reconstructed therefrom, and an already determined nuclear medical image data set.

Abstract: Systems and methods include acquisition of magnetic resonance data of a subject disposed in a first position, acquisition of positron emission tomography data of imaging hardware and of the subject disposed substantially in the first position, generation of a subject attenuation correction map of the subject based on the magnetic resonance data, determination of an imaging hardware attenuation correction map associated with the imaging hardware, determination of a target location and orientation of the imaging hardware attenuation correction map with respect to the positron emission tomography data and based on the positron emission tomography data and on the subject attenuation correction map, and application of attenuation correction to the positron emission tomography data based on the imaging hardware attenuation correction map in the target location and orientation and the subject attenuation correction map to generate attenuation-corrected positron emission tomography data.

Abstract: Embodiments of the present disclosure provide a method and apparatus for correcting a distorted document image, where the method for correcting a distorted document image includes: obtaining a distorted document image; and inputting the distorted document image into a correction model, and obtaining a corrected image corresponding to the distorted document image; where the correction model is a model obtained by training with a set of image samples as inputs and a corrected image corresponding to each image sample in the set of image samples as an output, and the image samples are distorted. By inputting the distorted document image to be corrected into the correction model, the corrected image corresponding to the distorted document image can be obtained through the correction model, which realizes document image correction end-to-end, improves accuracy of the document image correction, and extends application scenarios of the document image correction.

Abstract: Provided are embodiments of a method for performing automatic analysis of cross-sections of micro-channels. Embodiments includes receiving tomography scan data, aligning the tomography scan data, and extracting channels from a slice of the tomography scan data to create an isolated slice of the extracted channel. Embodiments also include determining surface voxels for the extracted channels, and determining an area defined within the surface voxels for each of the extracted channels. Embodiments include determining a contribution of the surface voxels for each of the extracted channels to the cross-section of the extracted channels, and outputting cross-section information based on the contribution of the surface voxels. Also provided are embodiments of a system for performing automatic analysis of cross-sections of micro-channels.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, the number of iterations in deep learning-based reconstruction may be reduced by including a learnable extrapolation in one or more iterations. Regularization may be provided in fewer than all of the iterations of the reconstruction. The result of either approach alone or both together is better quality reconstruction and/or less computationally expensive reconstruction.

Abstract: A system for consistently imaging objects may include an imaging device that presents a visual guide on a display for aligning with a target object, and that uses sensors of the imaging device to provide exact direction for correctly aligning the visual guide with the target object prior to capturing an image of the target object. The system may include a device that receives a particular image of a particular object, selects a model that defines positional commonality or visual characteristic commonality between a set of images of the particular object or a particular object type that includes the particular object, and that generates an edited image by correcting one or more deviations between positioning of the particular object in the particular image and the positional commonality specified in the model, or between visual characteristics of the particular image and the visual characteristic commonality specified in the model.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.

Abstract: A control device including: a breast thickness acquisition unit that acquires a thickness of a breast in a pressed state by a pressing member; a depth information acquisition unit that, in a case where an ultrasound image of the breast in the pressed state is captured, acquires depth information indicating a depth to which imaging by an ultrasonography apparatus which captures the ultrasound image is possible; a deriving unit that derives imaging information indicating whether or not capture of an ultrasound image having predetermined accuracy or higher is possible by the ultrasonography apparatus on the basis of the thickness of the breast acquired by the breast thickness acquisition unit and the depth information acquired by the depth information acquisition unit; and an output unit that outputs the imaging information derived by the deriving unit.

Abstract: Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function.

Abstract: Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

Abstract: A method for processing image data having noise and information, including: acquiring input raw image data having pixels of an image sensor used to take the image data, processing the input data, and outputting processed image-output data. The step of acquiring input data includes acquiring an input-noise model from the input data, and the step of processing the input raw image data includes a preprocessing operation and determining an output-noise model adapted to reflect noise in the output data, and producing output raw-image data consistent with the output-noise model, and the step of outputting the processed image data includes storing and/or transmitting the output raw image data and the output-noise model, which together form the output data, in a manner linking the output raw image data to the output-noise model, thereby allowing processing of the output data, as input data, such that the processing is adapted for pipeline processing.

Abstract: A method of visualization, characterization, and detection of objects within an image by applying a local micro-contrast convergence algorithm to a first image to produce a second image that is different from the first image, wherein all like objects converge into similar patterns or colors in the second image.

Abstract: Method of segmenting anatomical structures such as organs in 3D scans in an architecture that combines U-net, time-distributed convolutions and bidirectional convolutional LSTM.

Abstract: The invention discloses a limited-angle CT reconstruction method based on Anisotropic Total Variation. According to the method, through an image reconstruction model using low dose and sparse-view-angle CT images, a fast iterative reconstruction algorithm is combined with an Anisotropic Total Variation method. The problems that in an existing limited-angle CT reconstruction method are effectively solved, such as partial boundary ambiguity, slow convergence speed and unable to accurately solve. In the process of solving the model, the slope filter is introduced in the Filtered Back-Projection to preprocess the iterative equation, and the Alternating Projection Proximal is used to solve the iterative equation, and the iteration is repeated until the termination condition is satisfied; the experimental comparison with the existing reconstruction methods shows that the invention can achieve better reconstruction effect.

Abstract: The disclosure relates to a system and method for correcting PET image data. PET image data of a first part of a subject may be obtained. CT image data of a second part of the subject may be obtained. The first part may include the second part. PET voxel data of the first part may be obtained based on the PET image data of the first part. A relationship between the CT image data and PET voxel data of the second part may be determined. CT image data of a third part of the subject may be determined based on the relationship and PET voxel data of the third part. The first part may include the third part. An attenuation map may be determined based on the CT image data of the second part and the third part. The PET image data of the first part may be corrected based on the attenuation map.

Abstract: There is provided a method, comprising: accessing medical images of subjects, depicting contrast phases of contrast administered to the respective subject, accessing for a first subset of the medical images, metadata indicating a respective contrast phase, wherein a second subset of the medical images are unassociated with metadata, mapping each respective contrast phase of the contrast phases to a respective time interval indicating estimated amount of time from a start of contrast administration to time of capture of the respective medical image, creating a training dataset, by labelling images of the first subset with a label indicating the respective time interval, and including the second subset as non-labelled images, and training the ML model using the training dataset for generating an outcome of a target time interval indicating estimated amount of time from the start of contrast administration, in response to an input of a target medical image.

Abstract: A method for providing guidance in Cardiac Resynchronization Therapy (CRT) comprising: a) providing image data sets of the heart of a patient; b) creating a three-dimensional (3D) anatomical model of the cardiac structures; c) calculating myocardium infarct scar distribution; d) calculating heart mechanical activation data; e) superimposing the scar distribution and the mechanical activation data on the 3D anatomical model to obtain a 3D roadmap model including anatomical geometry and mechanical activation data. A corresponding device and computer program are also disclosed.

Abstract: An image analysis system including an image gathering unit that gathers a high-altitude image having multiple channels, an image analysis unit that segments the high-altitude image into a plurality of equally size tiles and determines an index value based on at least one channel of the image where the image analysis unit identifies areas containing anomalies in each image.

Abstract: Methods and systems generate synthetic late gadolinium enhancement (LGE) magnetic resonance images using a magnetic resonance fingerprinting (MRF) acquisition. From a single acquisition, MRF image data is obtained, including co-registered T1 and T2 tissue property maps. Different tissue regions of interest are identified, such as viable myocardium, scar, and blood and T1 and T2 values for each are determined. Based on these, different sets of pulse sequence parameters are determined, e.g., using different synthetic image contrast models receiving the MRF image data. Synthetic LGE images at different contrasts are generated as a result, including a synthetic bright-blood LGE image, a synthetic dark-blood/gray-blood LGE image, and a synthetic optimized imaged.

Abstract: Techniques for performing Internet of Things (IoT) device identification are disclosed. Information associated with a network communication of an IoT device is received. A determination of one or more confidence scores that represent how well the received information matches respective one or more network behavior pattern identifiers is made. A determination is made that each one of the one or more determined confidence scores is below a threshold. In response to determining that each of the one or more determined confidence scores is below the threshold, a two-part classification process is performed, where a first portion includes an inline classification, and a second portion includes a subsequent verification of the inline classification. A result of the classification process is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: Disclosed herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.

Abstract: Embodiments described herein provide techniques enable a graphics processor to continue processing operations during the reset of a compute unit that has experienced a hardware fault. Threads and associated context state for a faulted compute unit can be migrated to another compute unit of the graphics processor and the faulting compute unit can be reset while processing operations continue.

Abstract: An ultrasonic wave diagnostic apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires a plurality of pieces of volume data obtained through image capturing of a region including the cardiac ventricle of a subject for a predetermined duration. The processing circuitry estimates motion of tissue of the cardiac ventricle by using the pieces of volume data. The processing circuitry calculates, based on a result of the estimation, cardiac ventricle information indicating at least one of wall motion information and volume information related to the cardiac ventricle. The processing circuitry calculates, based on a result of the estimation, annulus information related to the size or shape of an annulus position of a valve related to inflow or outflow of blood current at the cardiac ventricle. The processing circuitry outputs the cardiac ventricle information and the annulus information.

Abstract: Apparatus, systems, and methods to deliver point of care alerts for radiological findings are disclosed. An example imaging apparatus includes an image data store, an image quality checker, and a training learning network. The example image data store is to store image data acquired using the imaging apparatus. The example image quality checker is to evaluate image data from the image data store in comparison to an image quality measure. The example trained learning network is to process the image data to identify a clinical finding in the image data, the identification of a clinical finding to trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the image data.

Abstract: A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

Abstract: Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.

Abstract: Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition.

Abstract: An imaging system may include an imaging detector to capture an image of human tissue and a compression paddle situated apart from the imaging detector to compress the human tissue between the compression paddle and the imaging detector. A force sensor may generate a force signal indicating a measure of force applied superior to the human tissue. A movement detection circuit may filter a movement signal from the force signal indicating a measure of movement of the compressed human tissue. A movement analysis module may determine that the movement signal is beyond a movement threshold. An image correction module to perform a corrective action based upon the determination that the movement signal is beyond a movement threshold.