An example method includes acquiring a first image using a medical imaging modality that includes a two-dimensional field of view to include a patient and a multi-modal marker. A second image is acquired using the medical image modality. The second image includes the patient and the multimodal marker and is along a non-coincident angle with respect to the first image. Predetermined portions of the multi-modal marker are visible in the first and second images and have a known location and orientation with respect to at least one sensor detectable by a tracking system. The method also includes estimating a three-dimensional position for predetermined portions of the multi-modal marker. The method also includes determining an affine transformation for registering a three-dimensional coordinate system of the tracking system with a three-dimensional coordinate system of the medical imaging modality.

This application relates to a method for processing image data of a microbial culture medium to recognize colony forming unit (CFU). In one aspect, the method includes receiving, at a processor, captured image data of the microbial culture medium from a user device, and preprocessing, at the processor, the captured image data. The method may also include counting, at the processor, the number of CFUs included in the preprocessed image data to derive result data including the counted number of CFUs. The method may further include automatically inputting information included in the result data into a predetermined template to generate document data corresponding to the captured image data, and transmitting at least one of the result data or the document data to the user device.

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.

Surgical instruments and methods for performing an osteotomy are disclosed herein. A surgical instrument includes a body with a distal end, a proximal end, a first surface, and a second surface. The surgical instrument can include cutting burrs positioned on the first surface and/or the second surface. The surgical instrument can include cutting burrs positioned on the first surface and cutting blades positioned on the second surface.

Surgical instruments and methods for performing an osteotomy are disclosed herein. A surgical instrument includes a body with a distal end, a proximal end, a first surface, and a second surface. The surgical instrument can include cutting burrs positioned on the first surface and/or the second surface. The surgical instrument can also include cutting burrs positioned on the first surface and cutting blades positioned on the second surface.

In implementations of systems for generating candidate mirror snap points using determined axes of symmetry, a computing device implements a symmetry system to receive vector object data describing a set of points of a vector object. The symmetry system generates convex polygons that enclose the set of points and identifies a particular convex polygon that has a smallest area. A side of the particular convex polygon is determined as an axis of symmetry for the vector object. The symmetry system generates an indication for display in a user interface of a candidate snap point based on the axis of symmetry and a point of the set of points of the vector object.

A method of generating a composite image from multiple images captured for a subject is disclosed. In some embodiments, the method may include receiving, via an image capturing device, a plurality of sets of images of at least a portion of a subject. The images within a set of images may be captured at a plurality of vertical positions with respect to an associated fixed section of a horizontal plane. The method may further include generating a plurality of focus-stacked images corresponding to the plurality of sets of images, for example, by combining the images in the associated set of images. The method may further include aligning the plurality of focus-stacked images in the horizontal plane based on a horizontal coordinate transformation model to generate a composite image representing the subject.

A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.

A method of generating a composite image from multiple images captured for a subject is disclosed. In some embodiments, the method may include receiving, via an image capturing device, a plurality of sets of images of at least a portion of a subject. The images within a set of images may be captured at a plurality of vertical positions with respect to an associated fixed section of a horizontal plane. The method may further include generating a plurality of focus-stacked images corresponding to the plurality of sets of images, for example, by combining the images in the associated set of images. The method may further include aligning the plurality of focus-stacked images in the horizontal plane based on a horizontal coordinate transformation model to generate a composite image representing the subject.