Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: A cultivator tool includes a power head having a handle and a user interface. A motor is positioned within the power head and is configured to rotate a drive shaft about a drive axis. The cultivator further includes an attachment stem removably coupled to the power head at a first end and removably supporting a tool attachment unit at an opposite second end. A cover is receivable on the attachment stem between the tool attachment unit and the power head to cover a cutter rotatably supported on the tool attachment unit. The cover includes an aperture configured to receive a projection extending from the stem to limit rotation and sliding of the cover relative the tool attachment unit.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Systems and method for assisted catheter steering are provided. Instructions for steering a catheter within a patient are received. A graph defining paths between a plurality of configurations of a robotic catheter navigation system is constructed based on the received instructions. Each of the plurality of configurations are associated with a respective view of the patient. A path is determined in the graph to a target configuration of the plurality of configurations of the robotic catheter navigation system. The catheter is automatically steered within the patient based on the determined path in the graph to recover the respective view of the patient associated with the target configuration.

Abstract: A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.

Abstract: Systems and methods for training a machine learning based segmentation network are provided. A set of medical images, each depicting an anatomical object, in a first modality is received. For each respective medical image of the set of medical images, a synthetic image, depicting the anatomical object, in a second modality is generated based on the respective medical image. One or more augmented images are generated based on the synthetic image. One or more segmentations of the anatomical object are performed from the one or more augmented images using a machine learning based reference network. An uncertainty associated with segmenting the anatomical object from the respective medical image is computed based on results of the one or more segmentations. It is determined whether the respective medical image is suitable for training a machine learning based segmentation network based on the uncertainty.

Abstract: A tip of a vascular catheter is detected in a sequence of vessel image frames obtained using x-rays. Within the vessel image frames, a proximal point corresponding to a vessel ostium is determined. The sequence of vessel image frames are cropped to an image area surrounding the proximal point to generate a cropped sequence of cropped vessel image frames. Within cropped vessel image frames not indicative of a contrast medium, the tip of the vascular catheter is detected, and the detected tip of the vascular catheter is tracked.

Abstract: A computer-implemented pathological vessel guidance method includes the acts of generating a vessel roadmap library, the vessel roadmap library including a plurality of vessel roadmaps, wherein each vessel roadmap includes a vessel roadmap image and first and second alignment data; detecting, within the vessel roadmap images of the vessel roadmap library, a pathological vessel; obtaining a real-time fluoroscopy image and corresponding real-time first and second fluoroscopy information; selecting a vessel roadmap from the roadmap library based on comparing the first real-time fluoroscopy information with the first alignment data of each vessel roadmap of the roadmap library; overlaying the real-time fluoroscopy image with the selected vessel roadmap image of the vessel roadmap library; aligning the vessel roadmap image of the selected vessel roadmap and the real-time fluoroscopy image based on the second alignment data and the real time second fluoroscopy information; and providing guidance for a fluoroscopy o

Abstract: The present application relates to a computer-implemented dynamic vessel roadmapping method. The method comprises generating a vessel roadmap library, the vessel roadmap library including a plurality of vessel roadmaps, wherein each vessel roadmap comprises vessel roadmap image and first and second alignment data, obtaining a real-time fluoroscopy image and corresponding real-time first and second fluoroscopy information, overlaying a vessel roadmap image of the vessel roadmap library with the real-time fluoroscopy image based on the first alignment data, the second alignment data, and the real time first and second fluoroscopy information and aligning the vessel roadmap image and the real-time fluoroscopy image based on the second alignment data and the real time second fluoroscopy information, wherein the second alignment data is derived from the corresponding vessel roadmap image.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Systems and methods for tracking a location of an object of interest through a sequence of medical images are provided. First and second input medical images of a patient are received. The first input medical image comprises an annotation of a location of an object of interest. Features are extracted from the first and the second input medical images. A location of the object of interest in the second input medical image is determined using a machine learning based location predictor network based on the annotation of the location of the object of interest in the first input medical image and the extracted features from the first and the second input medical images. The location of the object of interest in the second input medical image is output.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: An early education toy is configured to be used with a plurality of balls, and includes: a housing having a ball inlet and a ball outlet, and including a slideway therein, the slideway having an end connected with the ball inlet and another end connected with the ball outlet; a rotating part pivotably arranged to a bottom of the housing; a blocking part arranged in the housing, at least part of the blocking part being arranged in the ball outlet or at the end of the slideway adjacent to the ball outlet and configured to reciprocate to stop or release the ball; a driving part connected with the rotating part and the blocking part, and configured to drive the blocking part to move and to drive the rotating part to rotate relative to the housing.

Abstract: Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions.

Abstract: Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Disclosed are an engineered immune killer cell, and a preparation method therefor and the use thereof. The engineered immune killer cell is prepared by inducing reprogrammed human T cell, retains the marker and function of the human T cell from which the engineered immune killer cell is derived, has the marker and function of an NK cell, and transfects and expresses, in an obtained immune killer lymphocyte, a CAR molecule which recognizes tumor and virus-associated antigens or a TCR molecule which specifically recognizes a tumor.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Provided are engineered human immune cells, a preparation method, and an application thereof. The engineered human immune cells are immune killer lymphocytes induced by reprogramming human T cells, from which the BCL11B gene is deleted. The engineered human immune cells retain markers and functions of the T cells from which they are derived and have markers and functions of NK cells. The reprogrammed engineered human immune cells can be used to prepare drugs for treating tumors and infectious diseases.

Abstract: Provided are a genetically modified immune cell, a preparation method therefor, and an application. The immune cell overexpresses HIL-6 and/or L-GP130. HIL-6 or L-GP130 continuous overexpression/conditionally induced overexpression in the immune cell reduces the side effects of CAR-T therapy while maintaining immune and anti-tumour effects, and has potential value in the treatment of malignant tumours and AIDS.