An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

Systems and methods for levitating populations of moieties, cells, or other such units using one or more magnets in a microfluidic environment are provided. These systems and methods may be used to, for example, separate or sort heterogeneous populations of the units from one another, to assembly a multi-unit assembly during the levitating of the units, and to evaluate samples at the point of care in real-time. These systems and methods may also utilize a frame that enables an imaging device, such as a smartphone, to capture the units in real time as they are manipulated in the system.

Mechanisms for identifying inconsistencies between volumes of medical images are provided. A plurality of volumes of medical images are received, each having a plurality of medical images of an anatomical structure. A plurality of first representation data structures are generated, each corresponding to a volume and having dimensional measurements of the anatomical structure at various locations. A reference data structure is generated, for the anatomical structure based on the first representation data structures, having second dimensional measurements derived from the first dimensional measurements. A discrepancy is detected between a second representation data structure and the reference data structure based on a comparison and a notification of the discrepancy where the notification identifies a type of the discrepancy.

In some embodiments, apparatuses and methods are provided herein useful to digitally measuring, or generating biomarkers. In some embodiments, a system for generating a movement-based biomarker comprises an imaging device configured to generate image data associated with a user, a control system communicatively coupled to the imaging device, the control system configured to identify, in the first image, a feature of the user, determine, in the first image, a first location parameter associated with the feature of the user, identify, in the second image, the feature of the user, determine, in the second image, a second location parameter associated with the feature of the user, calculate, based on the first location parameter associated with the feature of the user and the second location parameter associated with the feature of the user, movement of the feature of the user, and generate, using the movement of the feature of the user, the movement-based biomarker.

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

In one embodiment, a patient's brain is evaluated after onset of a stroke by capturing computed tomography angiography (CTA) images of the brain, analyzing the CTA images with a CTA image analysis program to evaluate the patient's brain, and generating results based upon the analysis that provide an assessment of the brain. In some cases, the CTA image analysis program comprises a machine-learning algorithm that has been trained on the results of perfusion imaging analysis.

Monitoring melt pool temperature in laser powder bed fusion by providing a build laser that produces a laser beam that is directed onto the melt pool and produces an incandescence that emanates from the melt pool, receiving the incandescence and producing a first image having a first spectral band and a second image having a second spectral band, and determining the ratio of said first image having a first spectral band and said second image having a second spectral band to monitor the melt pool temperature.

A system for determining accuracy of a surgical procedure to implant an implant on a patient bone. The system including at least one computing device configured to perform the following steps. Receive preoperative patient data including preoperative images of the patient bone and planned implant position and orientation data. Receive postoperative patient data including postoperative images of the patient bone and an implant implanted on the patient bone. Segment the patient bone and the implant from the postoperative images of the patient bone and the implant. Register separately the patient bone and the implant from the postoperative images to the patient bone from the preoperative images. And compare an implanted position and orientation of the implant from the postoperative images relative to the patient bone from the preoperative images to the planned implant position and orientation data relative to the patient bone from the preoperative images.

Disclosed herein is a method of treating a subject having arterial stenosis. The method comprises: (a) providing a plurality of image frames of an artery of the subject taken in sequence; (b) in a plurality of cross-sections of the artery, determining a maximum diameter and a minimum diameter of each of the plurality of cross-sections of the artery among the plurality of image frames of the step (a); (c) calculating an average vasodilation ratio of the artery base on the maximum diameter and the minimum diameter determined in the step (b); and (d) treating the subject based on the average vasodilation ratio calculated in the step (c), by implanting a stent to the subject when the average vasodilation ratio is equal to or greater than 0.2; or administering to the subject an effective amount of a vasodilator when the average vasodilation ratio is less than 0.2.

According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.