Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Abstract: A learning image generation device includes an image acquisition unit that acquires a learning image, and a variation learning image generation unit that generates a variation learning image by adding a variation in which an output of a model deviates from a target value to a pixel value of at least one pixel that constitutes the learning image in a case in which the learning image acquired by the image acquisition unit is input to the model.

Abstract: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: A method of finding a set of corresponding points in images to be registered. According to the method, an input unit of the system receives a first user input, indicative of a reference point in an intraoperative image. A processing unit sets a reference area surrounding the reference point and converts the image data points in the reference area to a intraoperative point cloud. The input unit receives a second user input, indicative of a candidate point in a preoperative image. The processing unit sets a search area surrounding the reference point and converts the image data points in the reference area to a preoperative point cloud. By comparing geometric feature descriptors of the image data points, the processing unit finds a target point in the preoperative image corresponding to the reference point and defines the points as set of corresponding points.

Abstract: A real time assay monitoring system and method can be used to monitor reagent volume in assays for fluid replenishment control, monitor assays in real-time to obtain quality control information, monitor assays in real-time during development to detect saturation levels that can be used to shorten assay time, and provide assay results before the assay is complete, enabling reflex testing to begin automatically. The monitoring system can include a real time imaging system with a camera and lights to capture images of the assay. The captured images can then be used to monitor and control the quality of the staining process in an assay, provide early assay results, and/or to measure the on-site reagent volume within the assay.

Abstract: The exemplified system and method facilitate an objective, non-invasive measurement of myelin quality and integrity in living brains based on isolation of myelin-specific magnetic relaxation constants in k-space. The system uses magnetic resonance (MR) signals to select signatures specific to, or associated with, myelin and its structure and to then encode the selected signatures into an image or model to which the quantitative myelin health information can be co-registered with 2D, 3D visualization, and tractography of the myelin-signal isolated MR information. The system also sets a model for digital hierarchical learning of biomedical signals in MR, and beyond, based on experimental data in which it executes the herein described signal isolation operations.

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: A method for reconstructing a target cardiac image is provided. The method may include: determining a quality index for each cardiac image of a plurality of cardiac images corresponding to one or more cardiac motion phases; determining a phase of interest base on the plurality of quality indexes; and obtaining the target cardiac image of the phase of interest.

Abstract: Disclosed are methods and devices of generating predicted brain images. The present disclosure provides a method of generating a predicted brain image. The method comprises: receiving a first brain image; encoding the first brain image to generate a latent vector; and decoding the latent vector and one or more conditional features to generate the predicted brain image. The first brain image is generated by a magnetic resonance imaging (MRI) method. The one or more conditional features include at least one of: an age in future, a gender, previous brain images, omics features, and medical history. The latent vector is multiplied by a first normal distribution.

Abstract: Methods and systems for performing motion correction on imaging data. The methods include accessing a set of imaging data. The set of imaging data includes first imaging data for a first time point, second imaging data for a second time point, and third imaging data for a third time point. A location of a region of interest (ROI) is identified in the different imaging data. Differences between the locations of ROI across the imaging data are determined and aggregated to generate an aggregate motion score for the respective imaging data in the set of imaging data. One of the imaging data is then selected as reference imaging data for motion correction based on the aggregate motion score. Motion correction of the set of imaging data is performed based on the selected reference imaging data. Similar comparisons on images may be performed for peak enhancement of MRI imaging data.

Abstract: The present disclosure is directed to systems and methods for reconstructing biomedical images. A projection preparer may identify a projection dataset derived from a tomographic biomedical imaging scan. An encoder-decoder model may reconstruct reconstructing tomographic biomedical images from projection data. The encoder-decoder model may include an encoder. The encoder may include a first series of transform layers to generate first feature maps using the projection dataset. The encoder-decoder may include a decoder. The decoder may include a second series of transform layers to generate second features maps using the first feature maps from the encoder. The system may include a reconstruction engine executable on the one or more processors. The reconstruction engine may apply the encoder-decoder model to the projection dataset to generate a reconstructed tomographic biomedical image based on the second feature maps generated by the decoder of the encoder-decoder model.

Abstract: A computer-implemented method for updating a boundary segmentation, the method including receiving image data and an original boundary segmentation including a plurality of boundary points. A plurality of edges in the image data is detected and used to generate an edge map. A confidence for a boundary point in the original boundary segmentation is computed where the confidence is based on a distance between the boundary point and an edge point associated with at least one of the plurality of edges of the edge map, and based on the confidence a classification of the boundary point is determined. An updated boundary segmentation based on the classification of the boundary point is generated and then output.

Abstract: An estimation device includes at least one processor, in which the processor functions as a learned neural network that derives a result of estimation of at least one emphasis image in which a specific composition of a subject including a plurality of compositions is emphasized from a simple two-dimensional image acquired by simply imaging the subject. The learned neural network is learned by using, as teacher data, two radiation images acquired by imaging the subject with radiation having different energy distributions and an emphasis image for learning in which the specific composition of the subject is emphasized, which is derived from the two radiation images.

Abstract: A computer-implemented method for a medical imaging device is disclosed. In an embodiment, the computer-implemented method includes receiving at least one item of user information describing a desired visualization format of the image dataset; providing at least one item of request information; describing required input data of at least one evaluation algorithm to be used; determining at least one first processing dataset corresponding to the visualization format according to the user information and at least one second processing dataset usable as input data for the respective evaluation algorithm according to the request information; applying the at least one evaluation algorithm to the respective second processing dataset to determine evaluation information and outputting the first processing dataset and the evaluation information.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.

Abstract: A method of performing a radiological biopsy and associated system includes scanning a living human subject with a CT scanner to locate coordinates of an area of potential pathology and then using the coordinates to direct synchrotron radiation to a location at, or proximat the coordinates to obtain a high-resolution image of the area of potential pathology. The CT scan is accomplished with a CT scanner such as a C-Arm, vertical or horizontal CT scanner. A synchrotron radiation source emits synchrotron radiation through the subject and is processed by a processing system. The method and system allow for concurrent or sequential scanning of the subject by the CT scanner and synchrotron radiation scanner. The resulting images provide histological resolution of areas of potential pathology.

Abstract: The invention provides, in some aspects, a system for implementing a rule derived basis to display image sets. In various embodiments of the invention, the selection of the images to be displayed, the layout of the images, as well as the rendering parameters and styles can be determined using a rule derived basis. The rules are based on meta data of the examination as well as image content that is being analyzed by neuronal networks. In an embodiment of the present invention, the user is presented with images displayed based on their preferences without having to first manually adjust parameters.

Abstract: Described here are systems and methods for a robust magnetic resonance elastography (“MRE”) imaging platform for rapid dynamic 3D MRE imaging. The imaging platform includes an MRE pulse sequence and advanced image reconstruction framework that work synergistically in order to greatly expand the domains where MRE can be deployed successfully.

Abstract: A method of detecting colon polyps disclosed in the present disclosure includes: (a) receiving an image captured by an endoscope inserted into colon of a test subject; (b) recognizing each image section including colonic mucosa and colonic blood vessels in the image; (c) determining whether a colonic vascular bed is disconnected in each image section; (d) displaying a first visual effect representing a vascular bed in which the colonic blood vessels are disconnected; and (e) displaying a second visual effect representing a continuous vascular bed of the colonic blood vessels, wherein operation (b) is configured to recognize each image section through a deep learning model, which is machine learned based on blood vessel data in a plurality of colonic images of the test subject obtained from external annotators, and a degree of disconnection of the vascular bed and a blood vessel pattern.

Abstract: The invention provides for a method of training a neural network (322) configured for providing a further processing location (326). The method comprises providing (200) a labeled medical image (100), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (102, 104, 106). The method further comprises inputting (202) the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (108). The method further comprises determine (204) the closest truth processing location (106) for the most likely trial processing location. The method further comprises calculating (206) an error vector (110) using the closest truth processing location and the most likely trial processing location. The method further comprises training (208) the neural network using the error vector.

Abstract: A computer-implemented magnetic resonance image optimisation method is disclosed. An image of an object is mapped using a static magnetic field and divided into a plurality of voxels. Each voxel is represented in a Euclidean n-dimensional space, where n?3, and clustered by grouping together voxels having similar characteristics to create homogenous clusters. The centre or centroid of each cluster is determined, and used, or the voxel closest to either the centre or the centroid is used, as a super-voxel in an optimisation procedure. An optimised diagnostic image of the object is then generated.

Abstract: A method for noise reduction in a low-dose X-ray image includes preprocessing for determining input data, at least one trained function for determining noise-reduced output data from the input data, and postprocessing for determining a result image from the output data. At least one result parameter specifying at least one desired result attribute of the result image is received or determined. The at least one result attribute is obtained by modifying the preprocessing to set a noise value of at least one first noise parameter. The noise value is determined from the result parameter. The noise value may be selected to differ from a reference value of the first noise parameter. Alternatively or additionally, the at least one result attribute is obtained by setting, according to the result parameter, the at least one trained function to one of a plurality of predefined noise values of at least one second noise parameter.

Abstract: Methods and systems for training a neural network model include augmenting an original training dataset to generate an augmented training dataset, by applying an image artifact to a portion of an original image of the original dataset to generate an artifact image. A target image is generated corresponding to the artifact image by deleting labels from the target image at the position of the artifact. A neural network model is trained using the augmented training dataset and the corresponding target image, the neural network model including a first output that identifies artifact regions and other outputs identifying objects.

Abstract: DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

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: To obtain functional information 110 energising a first set of x-ray emitters of the panel over a first period of time and directing the x-rays at the first object; using the detector to detect the x-rays 35 after passing through the first object; processing the detected x-rays to create a first set of images to obtain tomosynthesis data 100; energising a second set of x-ray emitters of the panel over a second period of time and directing the x-rays at the first object; using the detector to detect the x-rays after passing through the first object; processing the detected x-rays to create a second set of images to obtain tomosynthesis data, wherein the number of emitters used in the second period of time is less than used in the first period of time; and comparing at least some images from each set of images to provide functional information.

Abstract: A method for x-ray photon-counting data correction. The method includes generating, by a training data generation module, training input spectral projection data based, at least in part, on a reference spectral projection data. The training input spectral projection data includes at least one of a pulse pileup distortion, a charge splitting distortion, and/or noise. The method further includes training, by a training module, a data correction artificial neural network (ANN) based, at least in part, on training data. The data correction ANN includes a pulse pileup correction ANN, and a charge splitting correction ANN. The training data includes the training input spectral projection data and the reference spectral projection data.

Abstract: To provide a technique of stably obtaining quantitative parameter maps by preventing influences of artifacts caused by flow or body motion when calculated images with a plurality of parameters are generated at the same time. When a calculated image of a subject parameter or an apparatus parameter is generated by using a plurality of images obtained by performing imaging under different imaging conditions, an imaging condition under which the artifacts are easy to occur is examined in advance, and an imaging parameter set for quantitative parameter map calculation is optimized by excluding the imaging condition under which the artifact is easy to occur.

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

Abstract: A medical image processing apparatus comprises processing circuitry configured to: receive volume data; set a symmetry plane for the volume data; generate mirrored volume data by mirroring the volume data using the symmetry plane; and generate symmetrized volume data based on the volume data and the mirrored volume data.

Abstract: A system and method assist gastroscopic image diagnosis based on artificial intelligence. The processor in the system analyzes each video frame of a gastroscopic image using at least one medical image analysis algorithm and detects whether a finding suspected of being a lesion is present in the video frame. When the finding suspected of being a lesion is present in the video frame, the processor calculates the coordinates of the location of the finding suspected of being a lesion. The processor generates display information, including whether the finding suspected of being a lesion is present and the coordinates of the location of the finding suspected of being a lesion.

Abstract: A user, a manipulator such as a hand, and at least one entity such as a virtual or augmented reality object are in an interface such as a 3D environmental interface. The manipulation distance is the distance between a reference feature of the user and a manipulation feature of the manipulator. The entity distance is the distance between the reference feature and an entity feature of the entity. When the manipulation distance becomes greater than the entity distance, the entity is caused to fade, disappear, move out of the way, shrink, etc. so as to be less of an obstruction to the user's field of view, for example to avoid obstructing more distant entities. Other factors than the manipulation distance and entity distance may be considered in determining whether to reduce the obstructivity of the entity, and exceptions to the obstruction relation may be considered.

Abstract: An image processing system (IPS), comprising an input interface (IN) for receiving a projection image from a plurality of projection images of a movable object (PAT) acquired along different directions by an imaging apparatus (XA), the projection images defined in a projection domain spanned by a radiation sensitive surface of the detector (D). The system includes a motion checker (MC) configured to operate in the projection domain to decide whether the projection image is corrupted by motion of the object during acquisition.

Abstract: Disclosed herein is a medical image reconstruction apparatus for reconstructing a medical image to assist the reading of a medical image. The medical image reconstruction apparatus includes a computing system, which includes: a receiver interface configured to receive a first medical image to which a first reconstruction parameter adapted to diagnose or analyze a first type of lesion is applied; and at least one processor configured to generate a second reconstruction parameter to be applied to the first medical image in response to a diagnosis order for the diagnosis or analysis of a second type of lesion. The at least one processor provides the second reconfiguration parameter to a user via a user interface, or generates a second medical image for the diagnose or analysis of the second type of lesion by executing the second reconstruction parameter on the first medical image and provides the second medical image to the user.

Abstract: An exemplary system, method and computer-accessible medium for harmonizing neuromelanin (NM) data using combat directly on a NM database or using combat generated coefficients to harmonize future data can include, for example, receiving imaging information of a brain of the patient(s), from one MRI scanner, receiving imaging information of a brain of the patient(s), from a second MRI scanner and using combat to harmonize the data between scanners against a reference dataset. The Neuromelanin (NM) concentration of the patient(s) can then be determined based on the harmonized data. The NM concentration can be determined using a voxel-wise analysis procedure. The voxel-wise analysis procedure can be used to determine a topographical pattern(s) within a substantia nigra (SN) of the brain of the patient(s).

Abstract: A method and system for drawing a brain functional atlas. The method includes: initializing a brain functional atlas of an individual by using a brain functional atlas template to obtain an initial individualized brain functional atlas; dividing the initial individualized brain functional atlas into a plurality of large areas, each large area including a plurality of functional areas; entering iteration, each iteration process including calculating the connection degree between each voxel in each large area and each functional area in the large area in sequence, and adjusting each voxel to the functional area having the highest connection degree with the voxel until all voxels are adjusted; and when an ending condition is satisfied, ending the iteration to obtain a final individualized brain functional atlas.

Abstract: Embodiments of the disclosure provide a method, apparatus, and device for data processing. The method comprises: obtaining data of at least two different modalities, the data comprising at least first data and second data, the first data and second stored in respective data sources; determining an overlapping keyword correlating the first data and the second data by analyzing textual descriptions associated with the first data and second data and identifying the overlapping keyword as appearing in a textual description of the first data and in a textual description of the second data; and storing the first data and the second data in an associative manner based on the overlapping keyword, the associative manner generated using the overlapping keyword.

Abstract: A system and method for creating computed tomography (CT) images includes acquiring or accessing sinogram data and determining a photon count for the sinogram data. The method includes generating unbiased sinogram data from the sinogram data using an unbiased estimator and the photon count and reconstructing a CT image from the unbiased sinogram data.

Abstract: To reduce instances of patient call back examinations and longer acquisition times in MRI, caused by features noticed at the periphery of a prescribed field of view (FOV) image, an extended field of view (EFOV) image is generated by storing k-space data including oversampling data, in a Picture Archiving and Communication System (PACS) and/or technician workstation. The saved k-space data is repurposed to re-reconstruct the (EFOV) image before any cropping operation on the prescribed FOV. In another application, the extended (EFOV) image is generated by repurposing stored k-space oversampling data, from adjacent or overlapping fields of view (FOV). The disclosed device and method can be used advantageously in a multi-station MRI IT system and/or spinal MRI examinations.

Abstract: Aspects of the technology described herein relate to receiving an ultrasound image, automatically determining a location of a specific point on an anatomical structure depicted in the ultrasound image, and displaying an indicator of the location of the specific point on the anatomical structure on the ultrasound image. In some embodiments, the anatomical structure is a bladder. In some embodiments, the specific point is the centroid. In some embodiments, a statistical model determines the specific point. The indicator may be, for example, a symbol located at the specific point, a horizontal line extending through the specific point from one edge of the anatomical structure to another, and/or a vertical line extending through the specific point from one edge of the anatomical structure to another.

Abstract: A method and system are for analyzing a plurality of interconnected blood vessels, preferably for providing output data for the analysis of a plurality of interconnected blood vessels, e.g. for vessel segmentation, vessel labeling, and vessel occlusion detection. The output data include a graphical representation of a vessel tree model of a patient. The method of an embodiment, in the most general terms includes receiving input data, e.g. via an interface; generating output data by applying algorithmic operations to the input data; and providing the output data, e.g. via an interface.

Abstract: Embodiments of this application disclose methods, systems, and devices for image region localization and medical image processing. In one aspect, a method comprises acquiring three-dimensional images of a target body part of a patient. The three-dimensional images comprise a plurality of magnetic resonant imaging (MRI) modalities. The method comprises registering a first image set of a first modality with a second image set of a second modality. After the registering, image features of the three-dimensional images are extracted. The image features are fused to obtain fused features. The method also comprises determining voxel types corresponding to voxels in the three-dimensional images according to the fused features. The method also comprises selecting, from the three-dimensional images, target voxels having a preset voxel type, obtaining position information of the target voxels, and localizing a target region within the target body part based on the position information of the target voxels.

Abstract: An information processing apparatus, method, and non-transitory computer-readable storage medium that estimate a three-dimensional skeleton based on images captured from a plurality of viewpoints, and generate a skeleton model by modeling the three-dimensional skeleton. The information processing apparatus, method, and non-transitory computer-readable storage medium further model a joint in a spherical shape or a bone in a cylindrical shape in the three-dimensional skeleton, and set a radius of a sphere of the joint or a radius of a cylinder of the bone according to a distance between any one of the plurality of viewpoints or a virtual viewpoint and the joint or the bone.

Abstract: Data acquired from a scan of an object can be decomposed into frequency components. The frequency components can be input into a trained model to obtain processed frequency components. These processed frequency components can be composed and used to generate a final image. The trained model can be trained, independently or dependently, using frequency components covering the same frequencies as the to-be-processed frequency components. In addition, organ specific processing can be enabled by training the trained model using image and/or projection datasets of the specific organ.

Abstract: Systems, machine-readable media, and methods for ultrasound imaging can include acquiring three-dimensional data for one or more patient data sets and generating a three-dimensional environment based on one or more transition areas identified between a plurality of volumes of the three-dimensional data. A method can also include generating a set of probe guidance instructions based at least in part on the one or more transition areas and the plurality of volumes of the three-dimensional data, and acquiring, using an ultrasound probe, a first frame of two-dimensional data for a patient. The method can also include executing the set of probe guidance instructions to provide probe feedback for acquiring at least a second frame of two-dimensional data.

Abstract: Augmenting real-time views of a patient with three-dimensional (3D) data. In one embodiment, a method may include identifying 3D data for a patient with the 3D data including an outer layer and multiple inner layers, determining virtual morphometric measurements of the outer layer from the 3D data, registering a real-time position of the outer layer of the patient in a 3D space, determining real-time morphometric measurements of the outer layer of the patient, automatically registering the position of the outer layer from the 3D data to align with the registered real-time position of the outer layer of the patient in the 3D space using the virtual morphometric measurements and using the real-time morphometric measurements, and displaying, in an augmented reality (AR) headset, one of the inner layers from the 3D data projected onto real-time views of the outer layer of the patient.

Abstract: A system and method is provided for high fidelity multi-energy CT processing. This system and method exploits prior knowledge, where prior knowledge may include redundant information existing in the CT images, such as spatial redundancy between a thick slice and a thin slice encompassed by or close to the thick slice, or the spatiospectral redundancy between the image output of multi-energy CT processing and the source multi-energy CT images. The system and method retains structural details, spatial resolution, spectral fidelity, and noise texture while achieving noise reduction. The method reduces image noise and increases the contrast-to-noise ratio in processed images, while simultaneously maintaining image details and natural appearance of the image to enhance detectability and facilitate reader acceptance.

Abstract: Systems and methods are provided for medical diagnosis and analysis using mixed model inversions. For example, a medical analyzer using mixed model inversions according to an embodiment of the present disclosure can be used to diagnose traumatic brain injury (TBI), which allows for isotropic and anisotropic inversions to be performed, enabling more accurate information about brain stiffness to be obtained.

Abstract: In image processing system comprises an input configured to receive an image signal, the image signal including a plurality of frames of image data; and a processor configured to automatically determine an image classification based on at least one frame of the plurality of frames, and dynamically generate a mapping metadata based on the image classification. The processor includes determination circuitry configured to determine a content type for the image signal; segmentation circuitry configured to segment the image data into a plurality of feature item regions, based on the content type; extraction circuitry configured to extract at least one image aspect value for respective ones of the plurality of feature item regions.

Abstract: Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.