Abstract: Some aspects of the technology described herein relate to configuring an ultrasound device to perform a three-dimensional ultrasound imaging sweep, and displaying ultrasound images and segmented portions of the ultrasound images as the ultrasound images are collected during the three-dimensional ultrasound imaging sweep. Certain aspects relate to displaying an ultrasound image collected by an ultrasound device and configuring the ultrasound device to perform a three-dimensional ultrasound imaging sweep based on the ultrasound image collected by the ultrasound device.

Abstract: Aspects of the technology described herein include determining, during ultrasound imaging, that an anatomical region is clipped by a field of view of an ultrasound image, and providing a notification, during the ultrasound imaging, that the anatomical region is clipped by the field of view of the ultrasound image. Aspects of the technology described herein also include determining that an anatomical region is clipped by a field of view of at least one ultrasound image collected during a three-dimensional ultrasound imaging sweep, and providing a notification that the anatomical region is clipped by the field of view of the at least one ultrasound image collected during the three-dimensional ultrasound imaging sweep.

Abstract: Aspects of the technology described herein relate to enhancing ultrasound data. Some embodiments include receiving ultrasound data and automatically determining, with a processing device, that the ultrasound data depicts an anatomical view or one of a set of anatomical views. Based on automatically determining that the ultrasound data depicts the anatomical view or one of the set of anatomical views, an option is enabled to perform ultrasound data enhancement specific to the anatomical view or the set of anatomical views. Based on receiving the selection of the option, the ultrasound data, a portion thereof, and/or subsequently-collected ultrasound data is enhanced using the ultrasound data enhancement specific to the anatomical view or the set of anatomical views.

Abstract: Various facial recognition systems may benefit from appropriate use of computer systems. For example, certain face analysis systems may benefit from an all-in-one convolutional neural network that has been appropriately configured. A method can include obtaining an image of a face. The method can also include processing the image of the face using a first set of convolutional network layers configured to perform subject-independent tasks. The method can further include subsequently processing the image of the face using a second set of convolutional network layers configured to perform subject-dependent tasks. The second set of convolutional network layers can be integrated with the first set of convolutional network layers to form a single convolutional neural network. The method can additionally include outputting facial image detection results based on the processing and subsequent processing.

Abstract: Aspects of the technology described herein include determining, during ultrasound imaging, that an anatomical region is clipped by a field of view of an ultrasound image, and providing a notification, during the ultrasound imaging, that the anatomical region is clipped by the field of view of the ultrasound image. Aspects of the technology described herein also include determining that an anatomical region is clipped by a field of view of at least one ultrasound image collected during a three-dimensional ultrasound imaging sweep, and providing a notification that the anatomical region is clipped by the field of view of the at least one ultrasound image collected during the three-dimensional ultrasound imaging sweep.

Abstract: Some aspects of the technology described herein relate to configuring an ultrasound device to perform a three-dimensional ultrasound imaging sweep, and displaying ultrasound images and segmented portions of the ultrasound images as the ultrasound images are collected during the three-dimensional ultrasound imaging sweep. Certain aspects relate to displaying an ultrasound image collected by an ultrasound device and configuring the ultrasound device to perform a three-dimensional ultrasound imaging sweep based on the ultrasound image collected by the ultrasound device.

Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image.

Abstract: A system includes one or more processors configured to analyze obtained image data representing a rotor blade to detect a candidate feature on the rotor blade and determine changes in the size or position of the candidate feature over time. The one or more processors are configured to identify the candidate feature on the rotor blade as a defect feature responsive to the changes in the candidate feature being the same or similar to a predicted progression of the defect feature over time. The predicted progression of the defect feature is determined according to an action-guidance function generated by an artificial neural network via a machine learning algorithm. Responsive to identifying the candidate feature on the rotor blade as the defect feature, the one or more processors are configured to automatically schedule maintenance for the rotor blade, alert an operator, or stop movement of the rotor blade.

Abstract: Various facial recognition systems may benefit from appropriate use of computer systems. For example, certain face analysis systems may benefit from an all-in-one convolutional neural network that has been appropriately configured. A method can include obtaining an image of a face. The method can also include processing the image of the face using a first set of convolutional network layers configured to perform subject-independent tasks. The method can further include subsequently processing the image of the face using a second set of convolutional network layers configured to perform subject-dependent tasks. The second set of convolutional network layers can be integrated with the first set of convolutional network layers to form a single convolutional neural network. The method can additionally include outputting facial image detection results based on the processing and subsequent processing.

Abstract: A system includes one or more processors configured to analyze obtained image data representing a rotor blade to detect a candidate feature on the rotor blade and determine changes in the size or position of the candidate feature over time. The one or more processors are configured to identify the candidate feature on the rotor blade as a defect feature responsive to the changes in the candidate feature being the same or similar to a predicted progression of the defect feature over time. The predicted progression of the defect feature is determined according to an action-guidance function generated by an artificial neural network via a machine learning algorithm. Responsive to identifying the candidate feature on the rotor blade as the defect feature, the one or more processors are configured to automatically schedule maintenance for the rotor blade, alert an operator, or stop movement of the rotor blade.

Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image.