Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.

Abstract: A method of imaging a specimen container and/or specimen. The method includes providing a specimen container containing a specimen at an imaging location, providing one or more cameras configured to capture images at the imaging location, providing one or more light sources adjacent to the imaging location, illuminating the imaging location with the one or more light sources, and capturing multiple images including: specimen images of the image location at multiple different exposures, with the specimen container and specimen being present at the image location. Quality check modules and specimen testing apparatus including a quality check module are described herein, as are other aspects.

Abstract: A model-based method of inspecting a specimen for presence of an interferent (H, I, and/or L). The method includes capturing images of the specimen at multiple different exposures times and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, identifying a serum or plasma portion of the specimen, and classifying whether an interferent is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.

Abstract: A model-based method of inspecting a specimen for presence of one or more artifacts (e.g., a clot, bubble, and/or foam). The method includes capturing multiple images of the specimen at multiple different exposures and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, computing statistics of the optimally-exposed pixels to generate statistical data, identifying a serum or plasma portion of the specimen, and classifying, based on the statistical data, whether an artifact is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: A model-based method of classifying a specimen in a specimen container. The method includes capturing images of the specimen and container at multiple different exposures times, at multiple different spectra having different nominal wavelengths, and at different viewpoints by using multiple cameras. From the captured images, 2D data sets are generated. The 2D data sets are based upon selection of optimally-exposed pixels from the multiple different exposure images to generate optimally-exposed image data for each spectra. Based upon these 2D data sets, various components are classified using a multi-class classifier, such as serum or plasma portion, settled blood portion, gel separator (if present), tube, air, or label. From the classification data and 2D data sets, a 3D model can be generated. Specimen testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: A method of imaging a sample container and/or a specimen in a sample container. The method includes enclosing at least a portion of a sample container with a moveable hood, the moveable hood having a wall with one or more openings extending between an interior of the moveable hood and an exterior of the moveable hood. Image data of the sample container is generated using one or more imaging devices positioned exterior to the moveable hood. The one or more imaging devices have a line of sight to the sample container through the one or more openings. Automated specimen testing systems, optical characterization apparatus, and methods of measuring characteristics of sample containers are provided, as are other aspects.

Abstract: An optical characterization apparatus for imaging a specimen container containing a specimen. The optical characterization apparatus includes a moveable hood configured to move between an open state and a closed state relative to a specimen container imaging location and having an interior, wherein when the moveable hood is in the closed state, a specimen container positioned at the specimen container imaging location is at least partially located within the interior of the moveable hood. One or more optical devices coupled to or within the interior of the moveable hood are positioned, when the moveable hood is in the closed state, to allow imaging of a specimen container positioned at the specimen container imaging location. Automated specimen testing systems, optical characterization apparatus, and methods of measuring characteristics of specimen containers are provided, as are other aspects.

Abstract: Embodiments provide a method of using image-based tube top circle detection based on multiple candidate selection to localize the tube top circle region in input images. According to embodiments provided herein, the multi-candidate selection enhances the robustness of tube circle detection by making use of multiple views of the same tube to improve the robustness of tube top circle detection. With multiple candidates extracted from images under different viewpoints of the same tube, the multi-candidate selection algorithm selects an optimal combination among the candidates and provides more precise measurement of tube characteristics. This information is invaluable in an IVD environment in which a sample handler is processing the tubes and moving the tubes to analyzers for testing and analysis.

Abstract: A characterization apparatus including pattern generation. The characterization apparatus is configured to characterize a specimen and or a specimen container in some embodiments. The characterization apparatus includes an imaging location configured to receive a specimen container containing a specimen, one or more image capture devices located at one or more viewpoints adjacent to the imaging location, and one or more light panel assemblies including pattern generation capability located adjacent to the imaging location and configured to provide back lighting. Methods of imaging a specimen and/or specimen container using the pattern generation are described herein, as are other aspects.

Abstract: Maintenance carriers can include one or more tools to perform a maintenance operation. These carriers may include removable cartridges that include the tool or consumables, such as a cleaning fluid, compressed gas, or disposable items. Maintenance carriers can also be configured to move along with other carrier traffic in the automation system and may be selectively deployed.

Abstract: Methods for image-based detection of the tops of sample tubes used in an automated diagnostic analysis system may be based on a convolutional neural network to pre-process images of the sample tube tops to intensify the tube top circle edges while suppressing the edge response from other objects that may appear in the image. Edge maps generated by the methods may be used for various image-based sample tube analyses, categorizations, and/or characterizations of the sample tubes to control a robot in relationship to the sample tubes. Image processing and control apparatus configured to carry out the methods are also described, as are other aspects.

Abstract: An apparatus for characterizing a specimen and/or specimen container. The characterization apparatus includes an imaging location configured to receive a specimen container containing a specimen, a light source configured to provide lighting of the imaging location, and a hyperspectral image capture device. The hyperspectral image capture device is configured to generate and capture a spectrally-resolved image of a small portion of the specimen container and specimen at a spectral image capture device. The spectrally-resolved image data received at the spectral image capture device is processed by a computer to determine at least one of: segmentation of at least one of the specimen and/or specimen container, and determination of a presence or absence of an interferent, such as hemolysis, icterus, or lipemia. Methods of imaging a specimen and/or specimen container, and specimen testing apparatus including a characterization apparatus are described, as are other aspects.

Abstract: A quality check module for characterizing a specimen and/or a specimen container including stray light compensation. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more image capture devices configured to capture images of the imaging location from one or more viewpoints, and one or more light sources configured to provide back lighting for the one or more image capture devices, and one or more stray light patches located in an area receiving stray light from the one or more light sources enabling stray light affecting the images to be compensated for and to provide a stray light compensated image.

Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for Hemolysis, Icterus, and/or Lipemia, or Normal detection. The method captures one or more images of a labeled specimen container including a serum or plasma portion, processes the one or more images to provide segmentation data and identification of a label-containing region, and classifying the label-containing region with a convolutional neural network (CNN) to provide a pixel-by-pixel (or patch-by-patch) characterization of the label thickness count, which may be used to adjust intensities of regions of a serum or plasma portion having label occlusion. Optionally, the CNN can characterize the label-containing region as one of multiple pre-defined label configurations. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization method may be used to provide input to an HILN (H, I, and/or L, or N) detection method. The characterization method includes capturing one or more images of a labeled specimen container including a serum or plasma portion from multiple viewpoints, processing the one or more images to provide segmentation data including identification of a label-containing region, determining a closest label match of the label-containing region to a reference label configuration selected from a reference label configuration database, and generating a combined representation based on the segmentation information and the closest label match. Using the combined representation allows for compensation of the light blocking effects of the label-containing region. Quality check modules and testing apparatus and adapted to carry out the method are described, as are other aspects.

Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a specimen. The method includes capturing one or more images of a labeled specimen container including a serum or plasma portion, processing the one or more images with a convolutional neural network to provide a determination of Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N). In further embodiments, the convolutional neural network can provide N-Class segmentation information. Quality check modules and testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: Methods of calibrating a position of a component using onboard crush and crash sensors. The method includes providing the robot with gripper and crush and crash sensors, providing a calibration tool coupled to the gripper, and providing a component including a recess and crush zone. The method includes moving the gripper in a first Controller direction to sense contact between the calibration tool and crush zone, and recording the contact position. Likewise, the gripper is moved to insert the tool into the recess, followed by moving the gripper in second directions and sensing contact between the tool and recess, and moving the gripper in third directions and sensing contact between the tool and recess. Contact positions are recorded and processed to determine a surface location in the first direction and physical center of the recess. Robot calibration apparatus for carrying out the method are disclosed, as are other aspects.

Abstract: An analyzer system for in vitro diagnostics includes a sample handler module having a robot arm that delivers samples from drawers into carriers on a linear synchronous motor automation track. Samples are delivered via the automation track to individual track sections associated with individual analyzer modules. Analyzer modules aspirate sample portions directly from the sample carriers and perform analysis thereon.