Methods and apparatus for fine-grained HIL index determination with advanced semantic segmentation and adversarial training

Patent metadata

Field	Value
Publication number	US-11927736-B2
Application number	US-201917251744-A
Country	US
Kind code	B2
Filing date	Jun 10, 2019
Priority date	Jun 15, 2018
Publication date	Mar 12, 2024
Grant date	Mar 12, 2024

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.

First claim

What is claimed is:

1. A method of characterizing a specimen container, comprising:
capturing an image of the specimen container with an image capture device, the specimen container including a serum or plasma portion of a specimen therein;
training a deep semantic segmentation network with a discriminative network such that the deep semantic segmentation network generates a homogeneously structured output;
processing pixel data of the image of the specimen container using the deep semantic segmentation network executing on a computer to determine a classification index of the serum or plasma portion; and
outputting from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes.

2. The method of claim 1 , wherein the classification index further comprises an un-centrifuged class.

3. The method of claim 1 , wherein the processing pixel data comprises determining the classification index of the serum or plasma portion based on pixel color data from the image of the serum or plasma portion identified in the specimen container using the deep semantic segmentation network executing on the computer.

4. The method of claim 1 , further comprising processing the pixel data of the image of the specimen container using a front-end container segmentation network executing on the computer to determine a container type and a container boundary.

5. The method of claim 1 , wherein the deep semantic segmentation network comprises an architecture including at least ten dense block layers.

6. A method of characterizing a specimen container, comprising:
capturing an image of the specimen container with an image capture device, the specimen container including a serum or plasma portion of a specimen therein;
processing pixel data of the image of the specimen container using a front-end container segmentation network executing on a computer to determine a container type and a container boundary;
processing the pixel data of the image of the specimen container using a deep semantic segmentation network executing on the computer to determine a classification index of the serum or plasma portion;
inputting the container type and container boundary from the front-end container segmentation network via an additional input channel to the deep semantic segmentation network to determine the classification index of the serum or plasma portion; and
outputting from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes.

7. The method of claim 6 , further comprising training the deep semantic segmentation network with a discriminative network such that the deep semantic segmentation network generates a homogeneously structured output.

8. A method of characterizing a specimen container, comprising:
capturing an image of the specimen container with an image capture device, the specimen container including a serum or plasma portion of a specimen therein;
processing pixel data of the image of the specimen container using a deep semantic segmentation network executing on a computer to determine a classification index of the serum or plasma portion; and
outputting from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes; wherein:
the deep semantic segmentation network comprises an architecture including at least ten dense block layers; and
the at least ten dense block layers includes at least three dense block layers each having a maximum of four dense layers and at least two dense block layers each having a maximum of five dense layers.

9. A method of characterizing a specimen container, comprising:
capturing an image of the specimen container with an image capture device, the specimen container including a serum or plasma portion of a specimen therein;
processing pixel data of the image of the specimen container using a deep semantic segmentation network executing on a computer to determine a classification index of the serum or plasma portion; and
outputting from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes; wherein:
the deep semantic segmentation network comprises an architecture including at least ten dense block layers; and
the deep semantic segmentation network comprises an architecture including at least one dense block layer having 15 dense layers.

10. A method of characterizing a specimen container, comprising:
capturing an image of the specimen container with an image capture device, the specimen container including a serum or plasma portion of a specimen therein;
processing pixel data of the image of the specimen container using a deep semantic segmentation network executing on a computer to determine a classification index of the serum or plasma portion; and
outputting from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes; wherein:
the deep semantic segmentation network comprises an architecture including at least ten dense block layers; and
the deep semantic segmentation network comprises an architecture including five transition down layers followed by five transition up layers.

11. A quality check module, comprising:
a plurality of image capture devices operative to capture one or more images from one or more viewpoints of a specimen container containing a serum or plasma portion of a specimen therein; and
a computer coupled to the plurality of image capture devices, the computer configured and operative to:
train a deep semantic segmentation network with a discriminative network such that the deep semantic segmentation network generates a homogeneously structured output;
process pixel data of the one or more images of the specimen container using the deep semantic segmentation network executing on the computer to determine a classification index of the serum or plasma portion; and
output from the deep semantic segmentation network the classification index of the serum or plasma portion, wherein the classification index comprises hemolytic, icteric, lipemic, and normal classes, and each of the hemolytic, icteric, and lipemic classes comprises five to seven sub-classes.

12. The quality check module of claim 11 , wherein the computer is further configured and operative to output the classification index further comprising an un-centrifuged class.

13. The quality check module of claim 11 , wherein the computer is further configured and operative to process the pixel data of the one or more images using a front-end container segmentation network executing on the computer to determine a container type and a container boundary.

14. A specimen testing apparatus, comprising:
a track;
a carrier moveable on the track and

Assignees

• Siemens Healthcare Diagnostics Inc

Inventors

• Ma Kai
• Chang Yao-Jen
• Chen Terrence
• Pollack Benjamin S

Classifications

• G06V20/69

  Microscopic objects, e.g. biological cells or cellular parts · CPC title

• G02B21/34 Primary

  Microscope slides, e.g. mounting specimens on microscope slides · CPC title

• G01N35/00732

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

• G06F18/241

  relating to the classification model, e.g. parametric or non-parametric approaches · CPC title

• G06F18/2431

  Multiple classes · CPC title