A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

The field of view of an X-ray imaging system should be set appropriately to ensure that anatomical information of interest is not omitted. In particular, it is necessary to ensure that the operator of an X-ray system does not allow a patient to leave the X-ray imaging system until it is certain that the correct anatomy has been imaged. This application discusses a technique enable the visualization of a field of view boundary error caused by the incorrect configuration of an X-ray imaging system. Optionally, the boundary error is displayed either on a user display of a system console, or by projecting the field of view error onto the patient in the X-ray system. Thus, an operator of the system may be alerted to the presence of a boundary error, enabling a new X-ray exposure to be taken, if necessary.

The technology is provided to effectively visualize culture statuses related to a plurality of culture targets. Provided is a control device including a display control unit that controls dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, in which the display control unit controls comparative display of the culture statuses of a plurality of the culture targets. Furthermore, provided is a control method including controlling, by a processor, dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, and controlling the display further including controlling comparative display of the culture statuses of a plurality of the culture targets.

A method may include obtaining an image representing a region of interest (ROI) of an object. The ROI may include two or more sub-regions. The method may include determining an average value of quantitative indexes associated with elements in the image corresponding to a first region of the ROI. The method may include determining, for each of the two or more sub-regions of the ROI, a threshold based on the average value; identifying target elements in the image based on the thresholds of the two or more sub-regions. The method may include assigning a presentation value to each of at least some of the target elements based on the average value and the quantitative index of the each target element. The method may include generating a presentation of the image based on the presentation values.

The three-dimensional (3D) reconstruction of visible part of the human jaw is becoming required for many diagnostic and treatment procedures. The present invention improves upon Statistical Shape from Shading (SSFS) framework by using a novel approach to automatically extract prior information. This two-step framework consists of interdental gingiva regions extraction for each individual tooth and detection of the centerline across the jaw span. These two steps help extract the anatomical landmark points and detect the status of the jaw. Experimental results highlight the accuracy of the extracted prior information and how this information boosts recovering 3D models of the human jaw.

Aspects of the technology described herein relate to techniques for calculating, during imaging, a quality of a sequence of images collected during the imaging. Calculating the quality of the sequence of images may include calculating a probability that a medical professional would use a given image for clinical evaluation and a confidence that an automated analysis segmentation performed on the given image is correct. Techniques described herein also include receiving a trigger to perform an automatic measurement on a sequence of images, calculating a quality of the sequence of images, determining whether the quality of the sequence of images exceeds a threshold quality, and performing the automatic measurement on the sequence of images based on determining that the quality of the sequence of images exceeds the threshold quality.

A machine learning system may be used for determining if a segmentation of a medical image is a reasonable segmentation in the sense that it is a segmentation that could be made by a human user and does not contain any impossible combinations of pixel values. The method is enhanced by user input to avoid the impossible combinations.

The present disclosure provides systems and methods for training and/or employing machine-learned models (e.g., artificial neural networks) to diagnose chest conditions such as, as examples, pneumothorax, opacity, nodules or masses, and/or fractures based on chest radiographs. For example, one or more machine-learned models can receive and process a chest radiograph to generate an output. The output can indicate, for each of one or more chest conditions, whether the chest radiograph depicts the chest conditions (e.g., with some measure of confidence). The output of the machine-learned models can be provided to a medical professional and/or patient for use in providing treatment to the patient (e.g., to treat a detected condition).

The present disclosure relates to a method and a device for target tracking, an electronic apparatus and a storage medium. The method comprises the following steps: obtaining a first tracking parameter from a template image of a target object; tracking the target object in a current image based on the first tracking parameter to obtain a first predicted tracking result of the current image; determining a second tracking parameter based on the template image and history images of the target object, wherein the history images represent images prior to the current image and containing the target object; tracking the target object in the current image based on the second tracking parameter to obtain a second predicted tracking result of the current image; and obtaining a tracking result of the target object in the current image based on the first predicted tracking result and the second predicted tracking result.

A system and method for identifying an object to be picked up by a robot. The method includes obtaining a 2D red-green-blue (RGB) color image and a 2D depth map image of the objects using a 3D camera, where pixels in the depth map image are assigned a value identifying the distance from the camera to the objects. The method generates a segmentation image of the objects using a deep learning convolutional neural network that performs an image segmentation process that extracts features from the RGB image, assigns a label to the pixels so that objects in the segmentation image have the same label and rotates the object using the orientation of the object in the segmented image. The method then identifies a location for picking up the object using the segmentation image and the depth map image and rotates the object when it is picked up.