A method and system for detecting spinal stenosis is provided. The method may receive image data corresponding to a spine region of a patient. The method may also identify a spinal cord in the image data. The method may determine at least one compression of the spinal cord and may mark an anatomical element proximate to a location of the determined at least one compression to yield at least one marking. The method may generate a decompression plan based on the at least one marking.

A system configured to generate a three-dimensional scene of a physical environment. In some cases, the system may present the three-dimensional scene such that the user is able to view the model in various different types. The system may also allow the user to capture various measurements associated with the scanned physical environment by selecting different portions of the scene.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for escape detection and mitigation for aquaculture. In some implementations, a method includes obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure; providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images; generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and detecting a condition based at least on the value.

A computer-implemented system and method for predicting female sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.

Systems and methods of valve quantification are disclosed. In one embodiment, a method of mitral valve quantification is provided. The method includes generating a 3-D heart model, defining a 3-D mitral valve annulus, fitting a plane through the 3-D mitral valve annulus, measuring the distance between at least two papillary muscle heads, defining an average diameter of at least one cross section around the micro valve annulus, and determining a size of an implant to be implanted.

Systems and methods of valve quantification are disclosed. In one embodiment, a method of mitral valve quantification is provided. The method includes generating a 3-D heart model, defining a 3-D mitral valve annulus, fitting a plane through the 3-D mitral valve annulus, measuring the distance between at least two papillary muscle heads, defining an average diameter of at least one cross section around the micro valve annulus, and determining a size of an implant to be implanted.

The invention relates to an apparatus for optically monitoring the dosing of a liquid to be pipetted for an automatic analysis unit. The apparatus comprises a dosing device, comprising a pipetting needle for pipetting the liquid, a lighting device for illuminating a drop of the liquid adhering to the pipetting needle, a camera with a set of optics to capture an image of the drop of the liquid, and an evaluation device for characterizing the drop of liquid by means of an automatic analysis of the image of the drop of liquid.

The invention relates to an apparatus for optically monitoring the dosing of a liquid to be pipetted for an automatic analysis unit. The apparatus comprises a dosing device, comprising a pipetting needle for pipetting the liquid, a lighting device for illuminating a drop of the liquid adhering to the pipetting needle, a camera with a set of optics to capture an image of the drop of the liquid, and an evaluation device for characterizing the drop of liquid by means of an automatic analysis of the image of the drop of liquid.

The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification. For example, by combining both the static features of the cell along with information on the cell motility, classification can be performed to determine the type of cell present in addition to the state of the cell (e.g., diseased vs. healthy).

The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification. For example, by combining both the static features of the cell along with information on the cell motility, classification can be performed to determine the type of cell present in addition to the state of the cell (e.g., diseased vs. healthy).