Abstract: Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.

Abstract: This invention relates to pharmaceutical composition and methods of using RXR agonist and/or RAR agonist for the treatment or prevention of head and neck cancer.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RARB agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency. After smoking, high fat diet is said to be the second most lethal habit, causing 300,000 deaths each year in the U.S. alone. High fat diet leads to many health problems, including obesity, stroke, cancer, high blood pressure, diabetes, osteoarthritis, rheumatoid arthritis, multiple sclerosis, heart disease, and diseases in other organs such as liver and kidney.

Abstract: Various embodiments of the present disclosure provide improved systems and techniques for intelligently allocating cache requests to caches based upon the nature of the cache objects associated with the cache requests, and/or the condition of the caches in the distributed cache system, to facilitate cache system utilization and performance. In some embodiments, an allocation of cache requests may be performed in response to detection of a problem at one of a group of cache servers. For example, in some embodiments, a particular cache server may be entered into a safe mode of operations when the cache's ability to service cache requests is impaired. In other embodiments, an allocation of cache requests may be performed based on an cache object data type associated with the cache requests.

Abstract: Disclosed herein are techniques for enhancing the accuracy of atlas-based auto-segmentation (ABAS) using an automated structure classifier that was trained using a machine learning algorithm. Also disclosed is a technique for training the automated structure classifier using atlas data applied to the machine learning algorithm.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate a target in a three-dimensional (3D) volume. For example, an image acquisition device may provide a 3D medical image containing a region of interest of the target. A processor may then extract a plurality of two-dimensional (2D) slices from the 3D image. The processor may also determine a 2D patch for each 2D slice, wherein the 2D patch corresponds to an area of the 2D slice associated with the target. The processor may also convert the 2D patch to an adaptive filter model for determining a location of the region of interest.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate and track a moving, three-dimensional (3D) target in a patient undergoing image-guided radiation therapy. For example, an adaptive filter model for a region of interest in the patient may be received, wherein the adaptive filter model is based on the target to be tracked. An image acquisition device may obtain a two-dimensional (2D) slice of a region of interest in the patient. A processor may then apply the adaptive filter model to the 2D slice, wherein the adaptive filter model includes an offset value. The processor may also determine a location of the target in the 2D slice based on the adaptive filter model. The processor may also estimate a potential location of the target based on the offset value. The processor may then repeat one or more of the above steps to track the moving target during image-guided radiation therapy of the patient.

Abstract: Systems and methods for building keyword searchable audience based on performance ranking are provided. The system includes a processor and a non-transitory storage medium accessible to the processor. The system includes a memory storing a database comprising segment data and campaign data. A computer server is in communication with the memory and the database, the computer server programmed to: obtain a performance-lift vector for an audience segment; obtain a campaign vector using meta-data from the campaign data; obtain a keyword vector for the audience segment using the performance-lift vector and the campaign vector; receive an input from a user interface accessible to an advertiser; and search the segment data at least partially based on the input and the keyword vector for segments in the segment data.

Abstract: An advertiser budget is retrieved from a data store. A set of traffic sources associated with the advertiser budget are identified. An allocated budget for the set of traffic sources is generated by allocating at least part of the advertiser budget to the set of traffic sources. At least one advertisement is delivered via a traffic source of the set of traffic sources based at least in part on the allocated budget.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical images. Embodiments of the present disclosure may relate to a method for segmenting medical images. The method may be implemented by a processor device executing a plurality of computer executable instructions. The method may comprise receiving an image from a memory, and identifying at least one landmark point within the image. The method may further comprise selecting an image point in the image, and determining at least one feature for the image point relative to the at least one landmark point. The method may also comprise associating the image point with an anatomical structure by using a classification model based on the at least one determined feature.

Abstract: The present invention discloses a device and method for data matching and a device and method for network intrusion detection. The method for data matching includes: searching in a regular expression set one or more complex regular expressions causing a sharp increase in number of states generated based on a regular expression during interaction; constructing a corresponding simplified expression for each complex regular expression; compiling a simplified state machine; compiling one or more substate machines, wherein each of the one or more substate machines is compiled based on a corresponding one of the one or more complex regular expressions; and matching data based on the simplified state machine and the one or more substate machines. The present invention further discloses a device for data matching employing the method for data matching and a device and method for intrusion detection employing the device and method for data matching.

Abstract: Disclosed herein are techniques for translating a first image of a first type to a derived image of a second type. For example, a plurality of similarity indicators can be computed as between a plurality of patches of first image and a plurality of patches of a first atlas image, the first atlas image being of the first type. Weight factors can then be computed based on the computed similarity indicators. These weight factors can be applied to a plurality of data points of a second atlas image to compute a plurality of data points for the derived image such that each of at least a plurality of the data points for the derived image is a function of a plurality of the data points of the second atlas image. Such a technique can be used for pseudo-CT generation from an MR image.

Abstract: Techniques are provided that include identifying and recommending one or more user segments as an audience for a particular campaign, such as an online advertising campaign, such as even if historical performance information for the particular campaign is limited or unavailable. Similar campaigns to the particular campaign may be identified. High-performing user segments for the similar campaigns may be identified. From these, one or more predicted best-performing user segments for the particular campaign may be identified and recommended as an audience for the particular campaign.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.

Abstract: Disclosed herein are techniques for enhancing the accuracy of atlas-based auto-segmentation (ABAS) using an automated structure classifier that was trained using a machine learning algorithm. Also disclosed is a technique for training the automated structure classifier using atlas data applied to the machine learning algorithm.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RARB agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency. After smoking, high fat diet is said to be the second most lethal habit, causing 300,000 deaths each year in the U.S. alone. High fat diet leads to many health problems, including obesity, stroke, cancer, high blood pressure, diabetes, osteoarthritis, rheumatoid arthritis, multiple sclerosis, heart disease, and diseases in other organs such as liver and kidney.

Abstract: The present disclosure provides a system and method for suggesting a bidding keyword to an advertiser. The system includes a non-transitory processor-readable storage medium comprising a set of instructions for suggesting a bidding keyword to an advertiser; and a processor in communication with the storage medium. The processor is configured to execute the set of instruction to receive an advertisement creative from an advertiser; determine, based on the advertisement creative without using an externally input seed keyword, a recommended bidding keyword associated with the advertisement creative; and return the recommended keyword for online advertisement bidding.

Abstract: Disclosed herein are techniques for enhancing the accuracy of atlas-based auto-segmentation (ABAS) using an automated structure classifier that was trained using a machine learning algorithm. Also disclosed is a technique for training the automated structure classifier using atlas data applied to the machine learning algorithm.

Abstract: Disclosed herein are techniques for performing automated structure delineation on image data using trained landmark detectors and a shape refinement tool. The landmark detectors can be trained to detect a landmark in the image data based on image features that are indicative of intensity variations over a plurality of windows of the image data points. A machine-learning algorithm can be used to train the landmark detectors. The landmarks in the image data that are detected by the trained landmark detects can be used to initialize an iterative shape refinement to thereby compute a refined shape estimate for a structure of interest such as a prostate.

Abstract: A method for performing automatic contouring in a medical image. The method may include receiving an image containing a region of interest and determining a first contour of the region of interest using a boundary detector. The method may include refining the first contour based on a shape dictionary to generate a second contour of the region of interest and updating at least one of the boundary detector or the shape dictionary based on the second contour.