Abstract: The present invention is directed to a computing system and method for the real-time automatic identification and tracking of a medical instrument and optionally the identification of at least one anatomical object in an image generated by an imaging system. The method includes generating the image via the imaging system and providing the image to a processor of a computing system. Further, the method includes developing and training at least one machine-learned model to automatically identify or locate the medical instrument and optionally at least one machine-learned model to identify or locate the anatomical object and the surrounding tissue in the image. Moreover, the method includes automatically labeling the identified medical instrument and optionally the identified anatomical object and surrounding tissue on the image. Further, the method also includes displaying the labeled image to a user in real-time.

Abstract: A method for scanning, identifying, and navigating at least one anatomical object of a patient via an imaging system. Such system includes scanning the anatomical object via a probe of the imaging system, identifying the anatomical object via the probe, and navigating the anatomical object via the probe. Further, the method includes collecting data, inputting the collected data into a deep learning network configured to learn the scanning, identifying, and navigating steps. In addition, the method includes generating a probe visualization guide for an operator based on the deep learning network. Thus, the method also includes displaying the probe visualization guide to the operator wherein the probe visualization guide instructs the operator how to maneuver the probe so as to locate the anatomical object. In addition, the method also includes using haptic feedback in the probe to guide the operator to the anatomical object of the patient.

Abstract: The present invention is directed to a system and method for automatic detection, localization, and semantic segmentation of at least one anatomical object in a parameter space of an image generated by an imaging system. The method includes generating the image via the imaging system and providing the image of the anatomical object and surrounding tissue to a processor. Further, the method includes developing and training a parameter space deep learning network comprising convolutional neural networks to automatically detect the anatomical object and the surrounding tissue of the parameter space of the image. The method also includes automatically locating and segmenting, via additional convolutional neural networks, the anatomical object and surrounding tissue of the parameter space of the image. Moreover, the method includes automatically labeling the identified anatomical object and surrounding tissue on the image. Thus, the method also includes displaying the labeled image to a user in real time.

Abstract: The present invention is directed to a computing system and method for the real-time automatic identification and tracking of a medical instrument and optionally the identification of at least one anatomical object in an image generated by an imaging system. The method includes generating the image via the imaging system and providing the image to a processor of a computing system. Further, the method includes developing and training at least one machine-learned model to automatically identify or locate the medical instrument and optionally at least one machine-learned model to identify or locate the anatomical object and the surrounding tissue in the image. Moreover, the method includes automatically labeling the identified medical instrument and optionally the identified anatomical object and surrounding tissue on the image. Further, the method also includes displaying the labeled image to a user in real-time.

Abstract: The present invention is directed to a system and method for providing navigational directions to a user to locate a target anatomical object during a medical procedure via a medical imaging system. The method includes selecting an anatomical region surrounding the object; generating a plurality of real-time two-dimensional images of scenes from the anatomical region and providing the plurality of images to a controller; developing and training a deep learning network to automatically detect and identify the scenes from the anatomical region; automatically mapping each of the plurality of images from the anatomical region based on a relative spatial location and a relative temporal location of each of the identified scenes in the anatomical region via the deep learning network; and providing directions to the user to locate the object during the medical procedure based on the relative spatial and temporal locations of each of the identified scenes.

Abstract: A method for scanning, identifying, and navigating at least one anatomical object of a patient via an imaging system includes scanning the anatomical object via a probe of the imaging system, identifying the anatomical object via the probe, and navigating the anatomical object via the probe. Further, the method includes collecting data relating to operation of the probe during the scanning, identifying, and navigating steps. Moreover, the method includes inputting the collected data into a deep learning network configured to learn the scanning, identifying, and navigating steps. In addition, the method includes generating a probe visualization guide for an operator based on the deep learning network. Thus, the method also includes displaying the probe visualization guide to the operator via a user display of the imaging system, wherein the probe visualization guide instructs the operator how to maneuver the probe so as to locate the anatomical object.

Abstract: The present invention is directed to a method for scanning, identifying, and navigating anatomical object(s) of a patient via an articulating arm of an imaging system. The method includes scanning the anatomical object via a probe of the imaging system, identifying the anatomical object, and navigating the anatomical object via the probe. The method also includes collecting data relating to the anatomical object during the scanning, identifying, and navigating steps. Further, the method includes inputting the collected data into a deep learning network configured to learn the scanning, identifying, and navigating steps relating to the anatomical object. Moreover, the method includes controlling the probe via the articulating arm based on the deep learning network.

Abstract: The present invention is directed to a system and method for providing navigational directions to a user to locate a target anatomical object during a medical procedure via a medical imaging system. The method includes selecting an anatomical region surrounding the object; generating a plurality of real-time two-dimensional images of scenes from the anatomical region and providing the plurality of images to a controller; developing and training a deep learning network to automatically detect and identify the scenes from the anatomical region; automatically mapping each of the plurality of images from the anatomical region based on a relative spatial location and a relative temporal location of each of the identified scenes in the anatomical region via the deep learning network; and providing directions to the user to locate the object during the medical procedure based on the relative spatial and temporal locations of each of the identified scenes.

Abstract: The present invention is directed to a system and method for automatic detection, localization, and semantic segmentation of at least one anatomical object in a parameter space of an image generated by an imaging system. The method includes generating the image via the imaging system and providing the image of the anatomical object and surrounding tissue to a processor. Further, the method includes developing and training a parameter space deep learning network comprising convolutional neural networks to automatically detect the anatomical object and the surrounding tissue of the parameter space of the image. The method also includes automatically locating and segmenting, via additional convolutional neural networks, the anatomical object and surrounding tissue of the parameter space of the image. Moreover, the method includes automatically labeling the identified anatomical object and surrounding tissue on the image. Thus, the method also includes displaying the labeled image to a user in real time.