Methods and apparatus for label compensation during specimen characterization

Patent metadata

Field	Value
Publication number	US-11538159-B2
Application number	US-201816604128-A
Country	US
Kind code	B2
Filing date	Apr 10, 2018
Priority date	Apr 13, 2017
Publication date	Dec 27, 2022
Grant date	Dec 27, 2022

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.

First claim

What is claimed is:

1. A characterization method, comprising:
capturing one or more images from multiple viewpoints of a specimen container including a serum or plasma portion, wherein the specimen container includes one or more labels thereon;
processing the one or more images from the multiple viewpoints to provide segmentation data for each of the multiple viewpoints, the segmentation data including identification of a label-containing region;
providing a reference label configuration database including reference image data on multiple reference label configurations;
identifying and selecting a closest label match of the label-containing region to a reference label configuration selected from the multiple reference label configurations contained in the reference label configuration database; and
generating a combined representation based on the segmentation data and the closest label match.

2. The method of claim 1 , comprising determining at least one of:
hemolysis, icterus, and/or lipemia, or
normality of the serum or plasma portion taking into account the one or more labels by using the combined representation.

3. The method of claim 1 , wherein generating a combined representation comprises calculating, on a pixel by pixel basis, a difference between, a ratio between, or a concatenation of, the label-containing region to the reference label configuration selected from the multiple reference label configurations.

4. The method of claim 1 , wherein the reference label configuration database comprises image data regarding different reference label configurations.

5. The method of claim 4 , wherein the image data regarding the different reference label configurations has been entered prior to the processing of the one or more images from the multiple viewpoints.

6. The method of claim 4 , wherein the reference label configuration database is made up of sub-databases capable of being indexed by an indexer.

7. The method of claim 4 , wherein the reference label configuration database is made up of sub-databases indexed by tube size.

8. The method of claim 4 , wherein the reference label configuration database is made up of sub-databases indexed by label size.

9. The method of claim 4 , wherein the reference label configuration database is made up of sub-databases indexed by label location.

10. The method of claim 1 , wherein the identifying and selecting the closest label match the closest label match of the label-containing region to the reference label configuration comprises minimizing a difference between the label-containing region to the reference label configuration.

11. The method of claim 10 wherein determining a closest match comprises down scaling of the segmentation data of the label-containing region to match a relative scale of the reference label configuration database.

12. The method of claim 1 , wherein the segmentation data is generated on the serum or plasma portion through the label-containing region.

13. The method of claim 1 , comprising generating a semantic map from the segmentation data from each of the multiple viewpoints.

14. The method of claim 13 , comprising selecting a synthetic viewpoint that has maximum visibility of the serum or plasma portion.

15. The method of claim 1 , wherein the capturing the one or more images from the multiple viewpoints comprises backlighting with light sources comprising one or more spectra of R, G, B, white light, IR, and near IR.

16. The method of claim 15 , wherein the capturing the one or more images from the multiple viewpoints comprises exposure at different exposure times for each of the one or more spectra.

17. The method of claim 1 , wherein barcode data in the segmentation data of the label-containing region is ignored.

18. A quality check module, comprising:
a plurality of image capture devices arranged around an imaging location, and configured to capture multiple images from multiple viewpoints of a specimen container including one or more labels and containing a serum or plasma portion of a specimen; and
a computer coupled to the plurality of image capture devices and adapted to process image data of the multiple images, the computer configured and capable of being operated to:
process the multiple images from the multiple viewpoints to provide segmentation data for each of the multiple viewpoints, the segmentation data including identification of a label-containing region,
store a reference label configuration database including reference image data on multiple reference label configurations,
determine a closest label match of the label-containing region to a reference label configuration selected from the multiple reference label configurations contained in the reference label configuration database, and
generate a combined representation based on the segmentation data and the closest label match.

19. The quality check module of claim 18 , comprising determining HILN of the serum or plasma portion taking into account the label-containing region with an HILN classifier using the combined representation.

20. A specimen testing apparatus, comprising:
a track;
a carrier moveable on the track and configured to contain a specimen container including one or more labels and containing a serum or plasma portion of a specimen;
a plurality of image capture devices arranged around the track and configured to capture multiple images of the specimen container and specimen from multiple viewpoints; and
a computer coupled to the plurality of image capture devices and adapted to process image data of the multiple images, the computer configured and capable of being operated to:
process the multiple images from the multiple viewpoints to provide segmentation data for each of the multiple viewpoints, the segmentation data including identification of a label-containing region,
store a reference label configuration database including reference image data on multiple reference label configurations,
determine a closest label match of the label-containing region to a reference label configuration selected from the multiple reference label configurations contained in the reference label configuration database, and
generate a combined representation based on the segmentation data and the closest label match.

Assignees

• Siemens Healthcare Diagnostics Inc

Inventors

• Kluckner Stefan
• Wissmann Patrick
• Chang Yao-Jen
• Chen Terrence
• Pollack Benjamin S

Classifications

• G01N35/00732 Primary

  Identification of carriers, materials or components in automatic analysers · CPC title

• G06T7/0014 Primary

  using an image reference approach · CPC title

• G01N21/31

  Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry {(G01N21/72 takes precedence)} · CPC title

• G06F18/24

  Classification techniques · CPC title

• G01N2035/00752

  bar codes · CPC title