The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 12, 2024

Filed:

Jun. 10, 2019
Applicant:

Siemens Healthcare Diagnostics Inc., Tarrytown, NY (US);

Inventors:

Kai Ma, West Windsor, NJ (US);

Yao-Jen Chang, Princeton, NJ (US);

Terrence Chen, Princeton, NJ (US);

Benjamin S. Pollack, Jersey City, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G02B 21/34 (2006.01); G01N 35/00 (2006.01); G06F 18/214 (2023.01); G06F 18/241 (2023.01); G06F 18/2431 (2023.01); G06T 7/00 (2017.01); G06V 10/10 (2022.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G02B 21/34 (2013.01); G01N 35/00732 (2013.01); G06F 18/241 (2023.01); G06F 18/2431 (2023.01); G06T 7/0012 (2013.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G01N 2035/00752 (2013.01); G06F 18/214 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06V 10/16 (2022.01);
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.


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