Systems and methods are disclosed for treating teeth to correct for malocclusions. This may be accomplished by applying a series of labels to a digital dental model and applying a rolling ball process to identify tooth boundaries separating one tooth from a neighboring tooth and to also determine the crown/gum margin. The user may further assign regions to the dental model to indicate hard regions and soft regions. With the dental model labeled and defined, the user may then generate a treatment plan for moving the labeled and defined tooth or teeth relative to one another to correct for any malocclusions. Upon approval of the treatment plan, a series of 3D printed dental appliances or aligners to be worn in series by the patient may be fabricated to ultimately move the tooth or teeth to a desired position.

Systems and methods are described for the fully automatic, template-free locating and extracting of a plurality of two-dimensional projections of particles in a micrograph image. A set of reference images is automatically assembled from a micrograph image by analyzing the image data in each of a plurality of partially overlapping windows and identifying a subset of windows with image data satisfying at least one statistic criterion compared to other windows. A normalized cross-correlation is then calculated between the image data in each reference image and the image data in each of a plurality of query image windows. Based on this cross-correlation analysis, a plurality of locations in the micrograph is automatically identified as containing a two-dimensional projection of a different instance of the particle of the first type. The two-dimensional projections identified in the micrograph are then used to determine the three-dimensional structure of the particle.

The apparatus for an X-ray data generation according to an embodiment of the inventive concept includes a processor that receives 3D data to generate output data and a buffer, and the processor includes an extraction unit that extracts raw object data from the 3D data and projects the raw object data onto a 2D plane to generate first object data, an augmentation unit that performs data augmentation on the first object data to generate second object data, a composition unit that synthesizes the second object data and background data to generate composite data, and a post-processing unit that performs post-processing on the composite data to generate the output data, and the buffer stores a plurality of parameters related to generation of the first object data, the second object data, the composite data, and the output data.

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

The present invention relates to systems and methods for tissue processing and analysis. Tissue chambers are configured to allow single-container chemical processing, imaging, and wax embedding of tissue samples in a single container without manipulation between steps. Tissue chambers with features to support the tissue sample and allow fluid flow between the tissue sample and the tissue chamber surface are disclosed. The features may be index matched to sample structures of interest or dissolvable in clearing solution to allow for in-chamber imaging with minimal distortion. Specialized tissue processing and wax removal apparatuses are also disclosed including for use with tissue chambers having frangible portions to permit ease of wax removal.

The ultrasonic diagnostic apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to: acquire multiple position data associated with respective multiple two-dimensional image data of ultrasonic related to multiple cross sections; smooth the acquired multiple position data; and arrange the multiple two-dimensional image data in accordance with the smoothed multiple position data to generate volume data.

An information display method displays brain activity information calculated using a biological signal measured from a living body so as to be superimposed on a morphological image including a plurality of sectional images. The information display method includes: acquiring mask information representing an area belonging to a brain in the morphological image; and detecting one or more extremal value positions with spatial extremal values in the brain activity information based on the mask information. A sectional image corresponding to the one or more extremal value positions is acquired from the morphological image. A brain activity distribution corresponding to the sectional image is acquired. The brain activity distribution is displayed so as to be superimposed on the sectional image.

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

Methods and systems estimate crop coefficients of a crop. At least one image sensor system captures a plurality of multispectral images of the crop and image data is derived from the multispectral images. At least one vegetation index of the crop is determined based on image data in at least a first spectral band. The reflectance of the crop monotonically increases and reaches a reflectance of at least 20% for at least one wavelength in the first spectral band. A crop coefficient of the crop is estimated based on the determined at least one vegetation index.

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.