Abstract: A client type communication device for communicating with an access point type communication device is provided. The client type communication device comprises a communication unit adapted to use at least one of first communication resources for performing a random access communication if the client type communication device is in an associated mode regarding the access point type communication device, and to use at least one of second communication resources different from the first communication resources for performing a random access communication to the access point type communication device if the client type communication device is in a non-associated mode regarding the access point type communication device.

Abstract: A leg-protecting apparatus is arranged in close contact with a body surface of a wearer to cover a thigh, a knee, and a calf of the wearer, including a biomechanically protecting strap (A) arranged to correspond with structural locations and paths of tendons and ligaments of the knee and muscles proximal thereto during an exercise process. The strap comprises a cruciate ligament protecting strap (A-1), a patellar tendon protecting strap (A-2), a thigh muscle group protecting strap (A-3), and a calf muscle group protecting strap (A-4). Elastic moduli of the cruciate ligament protecting strap (A-1) and the patellar tendon protecting strap (A-2) has a step-change upon a change in a fabric tensile ratio caused by the knee bending of the wearer, wherein the step-change occurs after an initial low tensile modulus stage, during a tensile sudden-change stage, and before a high tensile modulus stage transitioning between the stages as knee angle decreases.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RAR? agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency.

Abstract: An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image.

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: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving a three-dimensional image acquired by an imaging device, and selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional image. The method may further include segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the plurality of stacks.

Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving a three-dimensional image acquired by an imaging device, and selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional image. The method may further include segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the plurality of stacks.

Abstract: This invention relates to methods of producing aerogels and composites thereof. In particular, the invention relates to methods of producing silica aerogels and composites thereof. The invention also relates to doped aerogels and doped silica aerogels. The method involves the use of alkaline solutions, and particularly aqueous alkaline solutions, during the aerogel drying process. The method is more energy efficient and cheaper than prior art methods.

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: Network attack defense includes: obtaining a set of one or more statistical attributes for a protected site by gathering statistics for a set of one or more site attributes of the protected site, the site attributes indicating an operation mode of the protected site; determining, based on the set of one or more statistical attributes, that the protected site is to transition from a current operation mode to a target operation mode, wherein the current operation mode has a current defense strategy different from a target defense strategy of the target operation mode; and if the protected site is to transition from the current operation mode to the target operation mode, transitioning from the current operation mode to the target operation mode and applying the target defense strategy for the protected site instead of the current operation mode.

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 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 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: Embodiments disclose a method and system for segmenting medical images. In certain embodiments, the system comprises a database configured to store a plurality of medical images acquired by an image acquisition device. The plurality of images include at least one first medical image of an object, and a second medical image of the object, each first medical image associated with a first structure label map. The system further comprises a processor that is configured to register the at least one first medical image to the second medical image, determine a classifier model using the registered first medical image and the corresponding first structure label map, and determine a second structure label map associated with the second medical image using the classifier model.

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.

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.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RAR? agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency.

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.