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.

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: 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.

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 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: Embodiments are directed to a carrier configured to hold and transport a vessel with a barcode label in an automation system in in vitro diagnostics (IVD) environment, with a plurality of peripheral readers able to read the barcode label. The reading of the barcode label on the vessel loaded into the carrier is achieved without the need to align (either manually or automatically) the barcode to the peripheral readers. A dual spring arrangement provides for consistent vessel capture when loading the vessel into the carrier and for retention in the carrier during transport operations. A centrally-located spring housing provides efficient space utilization. A mirrored dual slot design is provided so that an unobstructed space for each slot is complimentary such that a completely unobstructed view of the vessel is constructed by imaging the same vessel in both slots (one after another) and fusing the resulting data.

Abstract: Methods of positioning a gripper to pick or place a specimen container from a sample rack. One method includes providing a robot including the gripper, the gripper moveable in a coordinate system by the robot and including gripper fingers, providing a sample rack including receptacles containing specimen containers, providing data, obtained by imaging, regarding the specimen containers in the sample rack, and dynamically orienting the gripper based upon the data. The data may include population and/or configuration data and the dynamic orientation may include gripper finger opening distance, gripper finger rotational position, and/or gripper offset distance. Gripper positioning apparatus for carrying out the method are disclosed, 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 operating a gripper are provided. The methods include providing a robot including the gripper, the gripper moveable by the robot and including gripper fingers, providing a sample rack including receptacles accessible by the gripper, at least some of the receptacles adapted to contain specimen containers, providing data, obtained by imaging, regarding the sample rack and the specimen containers therein, and determining, based on the data, an accessible target receptacle for one of a pick operation or a place operation. Apparatus and systems configured to carry out the methods are provided, as are other aspects.

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: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Specimen Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination.

Abstract: A method of characterizing a specimen as containing hemolysis, icterus, or lipemia is provided. The method includes capturing one or more images of the specimen, wherein the one or more images include a serum or plasma portion of the specimen. Pixel data is generated by capturing the image. The pixel data of the one or more images of the specimen is processed using a first network executing on a computer to predict a classification of the serum or plasma portion, wherein the classification comprises hemolysis, icterus, and lipemia. The predicted classification is verified using one or more verification networks. Quality check modules and specimen testing apparatus adapted to carry out the method are described, 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: Embodiments are directed to a combination of an automation system that continuously tracks the identity and positions of all of its pucks with a single sample identification station and covers/interlocks in order to provide sample chain of custody without the need to re-identify the sample at points of interaction (aspiration, de-capping, etc.). This eliminates the need to have sample identification stations at each interaction point. This reduction of hardware allows the system to be cheaper, smaller, and more reliable. It also allows not only the automation system, but also existing pre-analytical/analytical equipment connected to the automation system, to run more efficiently.

Abstract: One embodiment provides systems and methods for masking the effects of a flash on an operator including: a drawer system configured to receive a tray comprising one or more laboratory containers, wherein upon receiving the tray, the drawer system centers the tray underneath an image capture device; a flash device configured to activate, based on said centering, to illuminate the one or more laboratory containers; and an image capture device configured to capture an illuminated image of the one or more laboratory containers.

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: A model-based method of determining characteristics of a specimen container. The method includes providing a specimen container, capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths, selecting optimally-exposed pixels from the images at different exposure times at each spectra to generate optimally-exposed image data for each spectra, and classifying the optimally-exposed pixels as at least being one of tube, label or cap, and identifying a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A model-based method of determining characteristics of a specimen container cap to identify the container cap. The method includes providing a specimen container including a container cap; capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths; selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength; classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are numerous other aspects.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen for HILN (H, I, and/or L, or N). The method includes capturing images of the specimen at multiple different viewpoints, processing the images to provide segmentation information for each viewpoint, generating a semantic map from the segmentation information, selecting a synthetic viewpoint, identifying front view semantic data and back view semantic data for the synthetic viewpoint, and determining HILN of the serum or plasma portion based on the front view semantic data with an HILN classifier, while taking into account back view semantic data. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.