Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.

Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.

Abstract: A method for image segmentation includes receiving an input image (102). The method further includes obtaining a deep learning model (104) having a triad of predictors (116, 118, 120). Furthermore, the method includes processing the input image by a shape model in the triad of predictors (116, 118, 120) to generate a segmented shape image (110). Moreover, the method includes presenting the segmented shape image via a display unit (128).

Abstract: A method for image segmentation includes receiving an input image (102). The method further includes obtaining a deep learning model (104) having a triad of predictors (116, 118, 120). Furthermore, the method includes processing the input image by a shape model in the triad of predictors (116, 118, 120) to generate a segmented shape image (110). Moreover, the method includes presenting the segmented shape image via a display unit (128).

Abstract: A method and system for communicating medical data is presented. Patient data, scan parameters, and/or reference information may be received from a patient unit communicatively coupled to a remote unit over a communication network. The patient data may include at least an image corresponding to a patient. Further, one or more anatomical regions in the image may be identified. Additionally, ranks corresponding to the one or more anatomical regions may be computed based on the patient data, the scan parameters, and/or the reference information. Further, one or more portions of the image corresponding to the one or more anatomical regions may be iteratively transmitted from the patient unit to the remote unit based on the computed ranks.

Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.

Abstract: A method for detecting a lesion in an anatomical region of interest is presented. The method includes identifying one or more candidate mass regions in each of a plurality of 3D ultrasound images acquired at different view angles from the anatomical region of interest. Single-view features corresponding to each candidate mass region are identified. For a candidate mass region, a similarity metric between the single-view features corresponding to the candidate mass region and the single-view features corresponding to the other candidate mass regions is determined. The candidate mass region is classified based at least on the similarity metric. A system for imaging and a non-transitory computer readable media for detection of the lesion are also presented.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.

Abstract: A method and system for communicating medical data is presented. Patient data, scan parameters, and/or reference information may be received from a patient unit communicatively coupled to a remote unit over a communication network. The patient data may include at least an image corresponding to a patient. Further, one or more anatomical regions in the image may be identified. Additionally, ranks corresponding to the one or more anatomical regions may be computed based on the patient data, the scan parameters, and/or the reference information. Further, one or more portions of the image corresponding to the one or more anatomical regions may be iteratively transmitted from the patient unit to the remote unit based on the computed ranks.

Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.

Abstract: Embodiments of a method, a system, and a non-transitory computer readable medium for use in interactive magnetic resonance imaging are presented. An initial region of interest of a subject is scanned to acquire imaging data using an initial imaging protocol. Anatomical labeling information corresponding to a plurality of regions corresponding to the subject may be determined based on the acquired data and/or previously available information. Particularly, determining the anatomical labeling information may include identifying one or more features of interest corresponding to the initial region of interest. Further, input from an operator corresponding to a desired imaging task may be received interactively. The imaging protocol may be updated in real-time by selectively configuring one or more imaging parameters that optimize implementation of the desired imaging task based on the determined anatomical labeling information.

Abstract: Systems and methods for landmark correction in Magnetic Resonance Imaging (MRI) are provided. One method includes acquiring at least one calibration image or at least one localizer image of an object, identifying in the calibration or localizer images a region of the object as a reference point, wherein the reference point defines a landmark position. The method further includes determining an offset between an initial landmark position and the identified landmark position. The method also includes using the determined offset for MRI.

Abstract: A method for automated landmarking comprising obtaining one or more localizer images of a patient, comparing the one or more localizer images with a reference image, computing a difference between the one or more localizer images and the reference image, determining a desired position of the patient based on the computed difference, and maneuvering the patient, a support platform, or both the patient and the support platform to the desired position for imaging an anatomical region of interest in the patient.

Abstract: Systems and methods for landmark correction in Magnetic Resonance Imaging (MRI) are provided. One method includes acquiring at least one calibration image or at least one localizer image of an object, identifying in the calibration or localizer images a region of the object as a reference point, wherein the reference point defines a landmark position. The method further includes determining an offset between an initial landmark position and the identified landmark position. The method also includes using the determined offset for MRI.

Abstract: A method, system and apparatus for automatically setting a landmark for brain scans are described. In one embodiment, a method for medical image processing is described. The method comprises obtaining, by an imaging device, at least one image of a head of a subject. In addition, the method also comprises identifying, by a computer-based system, a reference feature in the at least one image associated with the head. The method also comprises automatically setting, by the computer-based system, a landmark based, at least in part, upon the reference feature.

Abstract: A method is provided for generating a metavolume for reconstructing an image. The method provides for acquiring a plurality of low-resolution images of an imaged volume, the plurality of low-resolution images obtained from one or more imaging planes and merging the plurality of low-resolution images using a statistical operation to generate a metavolume. Systems and computer programs that afford functionality of the type defined by this method may be provided by the present technique.

Abstract: A system and method for automatic computation of MR imaging scan parameters include a computer programmed to acquire a first set of MR data from an imaging subject, the first set of MR data comprising a plurality of slices acquired at a first field-of-view. The computer is also programmed to reconstruct the plurality of slices into a plurality of localizer images and identify a 3D object based on the plurality of localizer images. The computer is further programmed to prescribe a scan, execute the prescribed scan to acquire a second set of MR data, and reconstruct the second set of MR data into an image. The prescribed scan includes one of a reduced field-of-view based on a boundary of the 3D object and a shim region based on the boundary of the 3D object.