Abstract: In accordance with some embodiments, systems, methods, and media for automatically localizing and diagnosing thyroid nodules are provided. In some embodiments, a system for automatically diagnosing thyroid nodules comprises: an ultrasound machine; and a processor programmed to: receive a B-mode ultrasound of a thyroid from the ultrasound machine; provide the B-mode ultrasound to a classification model trained to automatically segment B-mode ultrasound; receive an output indicating which portions of the B-mode ultrasound correspond to a nodule; provide at least a portion of the B-mode ultrasound corresponding to the nodule to a second classification model trained to automatically classify thyroid nodules based B-mode, color Doppler, and shear wave elastography ultrasound; and receive, from the second trained classification model, an output indicative of the likelihood that the nodule is malignant.

Abstract: In accordance with some embodiments, systems, methods, and media for automatically localizing and diagnosing thyroid nodules are provided. In some embodiments, a system for automatically diagnosing thyroid nodules comprises: an ultrasound machine; and a processor programmed to: receive a B-mode ultrasound of a thyroid from the ultrasound machine; provide the B-mode ultrasound to a classification model trained to automatically segment B-mode ultrasound; receive an output indicating which portions of the B-mode ultrasound correspond to a nodule; provide at least a portion of the B-mode ultrasound corresponding to the nodule to a second classification model trained to automatically classify thyroid nodules based B-mode, color Doppler, and shear wave elastography ultrasound; and receive, from the second trained classification model, an output indicative of the likelihood that the nodule is malignant.

Abstract: A computer-aided system identifies aneurysm suspects in 3D image datasets. The system takes the raw image dataset as input and assigns one or more points of interest (POIs) in the image data. The system determines one or more features for each POI and identifies one or more aneurysm suspects from among the assigned POIs based on the determined features.

Abstract: A system and/or method automatically identifies one or more vascular regions in a medical image or set of medical images. For example, the system/method may automatically identify vascular structures as belonging to the left carotid, right carotid, and/or basilar vascular regions in the head. The system/method takes as input the medical image(s) and automatically identifies one or more vascular regions. The system/method may also automatically generate MIP renderings of the identified region or regions.

Abstract: Embodiments of the invention are generally related to graphical user interfaces (GUIs), and more specifically to selecting items from a radial menu. More specifically, embodiments of the invention provide a complex set of gestures which, when performed using a screen pointer, facilitate fast and accurate selection of radial menu items from a radial menu. The gestures may generally involve a first movement of the screen pointer to activate a desired radial menu item, and a second predefined movement of the screen pointer to select the radial menu item.

Abstract: A system and method for automatically generating sample points from a series of medical images and identifying a significant region are presented. An image acquisition system acquires the medical images of a region of interest (ROI) and an automated mask generator reviews the images to generate a parenchyma mask. Using the parenchyma mask, an automated sample point generator then detects portions of the medical images indicative of a material expected to be in a ROI and designates sample points therefrom. A target-tissue identification system uses the sample points to create a mathematical description of a target tissue and an enhanced target-tissue. A target-tissue change detection system then detects changes in the mathematical descriptions from those created using prior images. Finally, a significant region detector, which includes a training process to generate a quantitative definition of significance, automatically identifies a significant object in the series of medical images.

Abstract: A system and/or method automatically identifies one or more vascular regions in a medical image or set of medical images. For example, the system/method may automatically identify vascular structures as belonging to the left carotid, right carotid, and/or basilar vascular regions in the head. The system/method takes as input the medical image(s) and automatically identifies one or more vascular regions. The system/method may also automatically generate MIP renderings of the identified region or regions.

Abstract: Embodiments of the invention are generally related to graphical user interfaces (GUIs), and more specifically to selecting items from a radial menu. More specifically, embodiments of the invention provide a complex set of gestures which, when performed using a screen pointer, facilitate fast and accurate selection of radial menu items from a radial menu. The gestures may generally involve a first movement of the screen pointer to activate a desired radial menu item, and a second predefined movement of the screen pointer within a predefined selection zone to select the radial menu item.

Abstract: A computer-aided system identifies aneurysm suspects in 3D image datasets. The system takes the raw image dataset as input and assigns one or more points of interest (POIs) in the image data. The system determines one or more features for each POI and identifies one or more aneurysm suspects from among the assigned POIs based on the determined features.

Abstract: A cryoablation procedure at a surgery site is planned and simulated. One or more pre-procedure planning images of the surgery site are displayed on a user interface. Positioning of one or more virtual cryoprobes relative to the one or more pre-procedure planning images on the user interface is facilitated. Properties of the one or more virtual cryoprobes are selectable. Ice ball formation at the surgery site is then simulated based on the position and properties of the virtual cryoprobes. The simulated ice ball formation is based at least in part on tissue properties of the surgery site at each image unit of the one or more pre-procedure planning images. Boundaries of the simulated ice ball formation on the one or more pre-procedure planning images are then displayed.

Abstract: Embodiments of the invention are generally related to graphical user interfaces (GUIs), and more specifically to selecting items from a radial menu. More specifically, embodiments of the invention provide a complex set of gestures which, when performed using a screen pointer, facilitate fast and accurate selection of radial menu items from a radial menu. The gestures may generally involve a first movement of the screen pointer to activate a desired radial menu item, and a second predefined movement of the screen pointer within a predefined selection zone to select the radial menu item.

Abstract: Embodiments of the invention are generally related to graphical user interfaces (GUIs), and more specifically to selecting items from a radial menu. More specifically, embodiments of the invention provide a complex set of gestures which, when performed using a screen pointer, facilitate fast and accurate selection of radial menu items from a radial menu. The gestures may generally involve a first movement of the screen pointer to activate a desired radial menu item, and a second predefined movement of the screen pointer to select the radial menu item.

Abstract: A system and method for automatically generating sample points from a series of medical images and identifying a significant region are presented. An image acquisition system acquires the medical images of a region of interest (ROI) and an automated mask generator reviews the images to generate a parenchyma mask. Using the parenchyma mask, an automated sample point generator then detects portions of the medical images indicative of a material expected to be in a ROI and designates sample points therefrom. A target-tissue identification system uses the sample points to create a mathematical description of a target tissue and an enhanced target-tissue. A target-tissue change detection system then detects changes in the mathematical descriptions from those created using prior images. Finally, a significant region detector, which includes a training process to generate a quantitative definition of significance, automatically identifies a significant object in the series of medical images.

Abstract: An MRI system acquires NMR data and reconstructs an image. The image is enhanced prior to being displayed by automatically calculating a window level value and a window width value and mapping the reconstructed image through the resulting contrast window defined by those values.