Abstract: A calibration method is provided including identifying the imaging area on each light panel with respect to each imaging device. A center position of the imaging area of each light panel for each imaging device is determined. An optimal optical center of the imaging apparatus using the center position of the imaging area of each imaging device is determined. A tube calibration tool is installed in a carrier on a track, and the carrier is moved on the track so that a center of the tube calibration tool is located at a closest location to the optimal optical center of the imaging apparatus. The center of the tube calibration tool is used to determine a center of a region of interest (ROI) for backlight calibration. Methods for heath checking the calibration and apparatus used to carry out the calibration are provided as well as other aspects.

Abstract: A method of determining a 3D center location of a specimen container on a track. The method includes providing a calibration object on the track; providing an initially calibrated image capture device adjacent to the track; moving the calibration object to at least two different longitudinal positions along the track; capturing a first image with the calibration object located at the first longitudinal position; capturing a second image with the calibration object located at the second longitudinal position; and determining a three-dimensional path trajectory of a center location along the track based at least upon the first image and the second image. The method can be used to determine a 3D center location of a specimen container imaged anywhere within a viewing area. Characterization apparatus and specimen testing apparatus adapted to carry out the methods are described, as are other aspects.

Abstract: A method of aligning a component to a structure in a diagnostic laboratory system. The method includes aligning a position sensor to the structure; sensing a position of the component using the position sensor; and calculating the position of the component relative to the structure based at least in part on the sensing. Other methods, apparatus, and systems are disclosed.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: Apparatus for robotic arm alignment in an automated sample analysis system includes a robotic arm, a sample tube carrier, a plurality of optical components (including, e.g., one or more cameras), and a controller. The controller is operative to process images received from the optical components to determine a first set of coordinates of a first marker relative to the sample tube carrier and determine a second set of coordinates of a second marker relative to the robotic arm. The controller is further operative to adjust the position of the robotic arm and/or the sample tube carrier in response to an excessive offset between the first and second sets of coordinates. In some embodiments, a positioning tool includes the first and second markers thereon. Methods of robotic arm alignment with a sample tube carrier in an automated sample analysis system are also provided, as are other aspects.

Abstract: Methods of identifying a defect in a machine vision system. Embodiments of the method include providing a first imaging device having a first field of view; moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view using the first imaging device; and analyzing at least one of the plurality of images to identify one or more defects in the machine vision system. Systems and apparatus configured to carry out the methods are provided, as are other aspects.

Abstract: Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.

Abstract: A method of identifying a tube type. The method includes capturing one or more pixelated images of a cap affixed to a tube; identifying a color of one or more pixels of the pixilated image of the cap; identifying one or more gradients of a dimension of the cap; and identifying the tube type based at least on: the color of the one or more pixels, and the one or more gradients of a dimension of the cap. Apparatus adapted to carry out the method are disclosed as are other aspects.

Abstract: A method of calibrating an imaging device adapted to characterize a feature of a sample container, such as a cap color or cap type. The method includes providing a calibration tube including an imaging surface at an imaging location of a first imaging apparatus; illuminating the imaging surface with light emitted from multiple front light sources; adjusting a drive current to each of the multiple front light sources to establish a substantially uniform intensity of the imaging surface; recording drive current values for the multiple front light sources; replacing the calibration tube with a calibration tool having a calibration surface of a known reflectance; and measuring target intensity values of the calibration tool at the respective drive current values. Calibration tools, imaging apparatus, quality check modules, and health check methods are provided, as are other aspects.

Abstract: A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

Abstract: A method of characterizing a specimen in a specimen container includes capturing one or more images of the specimen container, wherein the one or more images include one or more objects of the specimen container, and wherein the capturing generates pixel data from a plurality of pixels. The method further includes identifying one or more selected objects from the one or more objects, displaying an image of the specimen container, and displaying, on the image of the specimen container, one or more locations of pixels used to identify the one or more selected objects. Other apparatus and methods are disclosed.

Abstract: Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.