Methods and apparatus for determining label count during specimen characterization

Abstract

A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels is provided. 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 labelthickness 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.

Description

technical field [0001] The present disclosure relates to methods and apparatus for characterizing sample containers and samples, and more particularly to determining whether a sample includes hemolysis (H), icterus (I), and / or lipemia (L) or is normal (N). method and apparatus. Background technique [0002] Automated test systems can be used to perform clinical chemistry or laboratory tests using one or more reagents to identify analytes or other components in samples such as urine, serum, plasma, interstitial fluid, cerebrospinal fluid, etc. Wait. For convenience and safety reasons, these samples may be contained within a sample container (eg, a blood collection tube). An assay or test reaction generates various changes that can be read and / or manipulated to determine the concentration of an analyte or other component present in a sample. Such sample containers may have one, sometimes more than one, label provided thereon. The label(s) may be the manufacturer's label an...

AI Technical Summary

Problems solved by technology

However, such a system may not be easily automated and may have other problems

For example, in some cases only a small portion of the serum or plasma fraction may be visible such that any readings taken on the serum or plasma fraction to determine H, I and / or L or N may not involve a high level of confidence

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