Abstract: An intake manifold body member has an upper surface with a MAP senor port, an air intake port, and a depending perimeter wall that defines an interior chamber. A MAP sensor port permits a MAP sensor to be inserted by a first distance into the interior chamber defined by the body member. A Map sensor shielding member depends from the body member by a second distance that is greater than the first distance and shields the MAP sensor.

Abstract: A method for aligning coordinate systems of user devices in an augmented reality system using somatic points of a user's hand as alignment markers. Images captured from multiple user devices are used to align the reference coordinate systems of the user devices to a common reference coordinate system. In some examples, user devices capture images of a hand of a user and use object recognition to identify somatic points as alignment markers. The somatic points of a user device are translated to a common reference coordinate system determined by another user device.

Abstract: An intake manifold includes a body member defining an interior chamber having an upper surface and a perimeter wall depending therefrom and extending to a perimeter edge, which extends in a plane. An air intake port extends through the body member and opens into the interior chamber. An EGR tube extends through an EGR tube port in the body member and the EGR tube opens into the interior chamber. A MAP sensor extends through a MAP port in the body member such that an interior portion of the MAP sensor is positioned in the interior chamber and extends from an interior surface of the body member upper surface by a first distance. A shield member includes a shield wall that depends from the interior surface of the upper surface by a distance larger than the first distance and defines a shielded area about the interior portion of the MAP sensor.

Abstract: An intake manifold includes a body member defining an interior chamber having an upper surface and a perimeter wall depending therefrom and extending to a perimeter edge, which extends in a plane. An air intake port extends through the body member and opens into the interior chamber. An EGR tube extends through an EGR tube port in the body member and the EGR tube opens into the interior chamber. A MAP sensor extends through a MAP port in the body member such that an interior portion of the MAP sensor is positioned in the interior chamber and extends from an interior surface of the body member upper surface by a first distance. A shield member includes a shield wall that depends from the interior surface of the upper surface by a distance larger than the first distance and defines a shielded area about the interior portion of the MAP sensor.

Abstract: A method for image analysis includes receiving compressed, encrypted image data from an imaging session at a healthcare location, unencrypting the image data, and identifying a characteristic of interest in the compressed image data. The method further includes annotating the compressed image data reflecting the identified characteristic of interest to generate computer annotations and streaming the computer annotations to the healthcare location as the imaging session is in progress.

Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.

Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Abstract: The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.

Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.

Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.

Abstract: A method of extracting mechanical activation of the left ventricle from a sequence of contrasted X-ray fluoroscopy images is provided. The method includes: processing the image sequence to perform segmentation of the coronary veins; annotating branches of the segmented coronary veins; tracking the positions of the annotations throughout the fluoroscopy image sequence; computing the principle components of the motion of the annotations from the tracked positions; projecting the motion of the annotations to the axis corresponding to a first principle component; and analyzing the resulting motion curves to identify a latest activating region of the left ventricle.

Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.

Abstract: A method of extracting mechanical activation of the left ventricle from a sequence of contrasted X-ray fluoroscopy images is provided. The method includes: processing the image sequence to perform segmentation of the coronary veins; annotating branches of the segmented coronary veins; tracking the positions of the annotations throughout the fluoroscopy image sequence; computing the principle components of the motion of the annotations from the tracked positions; projecting the motion of the annotations to the axis corresponding to a first principle component; and analyzing the resulting motion curves to identify a latest activating region of the left ventricle.

Abstract: A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.

Abstract: The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.

Abstract: In a method for visualization of scar tissue in medical imaging data of a heart, medical imaging data representing a heart myocardium and scar tissue within the heart myocardium are obtained and provided to a computer. The computer deviates the thickness of the myocardium into a number of layers and calculates the presence and distribution of scar tissue within each of the layers. The scar tissue is shown in a visualization of the myocardium; and a user is provided with controls to allow the user to select which of the layers of scar tissue is visualised.

Abstract: A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.

Abstract: In a method for visualization of scar tissue in medical imaging data of a heart, medical imaging data representing a heart myocardium and scar tissue within the heart myocardium are obtained and provided to a computer. The computer deviates the thickness of the myocardium into a number of layers and calculates the presence and distribution of scar tissue within each of the layers. The scar tissue is shown in a visualization of the myocardium; and a user is provided with controls to allow the user to select which of the layers of scar tissue is visualised.