Quantitative structural assay of a nerve graft

Patent metadata

Field	Value
Publication number	US-11847844-B2
Application number	US-202016996575-A
Country	US
Kind code	B2
Filing date	Aug 18, 2020
Priority date	May 28, 2015
Publication date	Dec 19, 2023
Grant date	Dec 19, 2023

Abstract

Techniques are described for determining the quality of a nerve graft by assessing quantitative structural characteristics of the nerve graft. Aspects of the techniques include obtaining an image identifying laminin-containing tissue in the nerve graft; creating a transformed image using a transformation function of an image processing application on the image; using an analysis function of the image processing application, analyzing the transformed image to identify one or more structures in accordance with one or more recognition criteria; and determining one or more structural characteristics of the nerve graft derived from a measurement of the one or more structures.

First claim

What is claimed is:

1. A method for assessing the quality of a nerve graft, the method comprising:
obtaining an image identifying laminin-containing tissue in the nerve graft;
creating a transformed image using a transformation function of an image processing application on the image;
using an analysis function of the image processing application, analyzing the transformed image to identify one or more structures based on one or more recognition criteria;
determining one or more structural characteristics of the nerve graft based on a measurement of one or more structures identified from the analysis, wherein the one or more structures identified from the analysis comprise at least a portion of an endoneurial tube and/or perineurium;
comparing the one or more structural characteristics to one or more of:
a qualitative assessment score;
one or more reference ranges indicating an acceptable structural characteristic of the nerve graft; or
a bioassay result of the nerve graft; and

assessing the quality of the nerve graft based upon, at least in part, one or more of the determined one or more structural characteristics of the nerve graft.

2. The method of claim 1 , wherein the identified one or more structures meet the one or more recognition criteria.

3. The method of claim 1 , wherein the identified one or more structures do not meet the one or more recognition criteria.

4. The method of claim 1 , wherein, before creating the transformed image, the method further comprises selecting one or more of an area of interest and/or a sampling window to delineate a selected image area, wherein the analyzing of the transformed image is performed only on the selected image area.

5. The method of claim 4 , wherein the area of interest comprises a nerve fascicle.

6. The method of claim 1 , wherein the one or more structures identified from the analysis is an endoneurial tube.

7. The method of claim 1 , wherein creating the transformed image comprises applying thresholding to the image.

8. The method of claim 7 , wherein applying the thresholding comprises applying one or more of a threshold method, a threshold color, a color space, and a dark background.

9. The method of claim 1 , wherein the one or more recognition criteria comprises a size range of the one or more structures.

10. The method of claim 9 , wherein the size range is from about 4.84 microns in diameter to about 16 microns in diameter.

11. The method of claim 1 , wherein the one or more recognition criteria comprise a circularity range of the one or more structures.

12. The method of claim 11 , wherein the circularity range is from about 0.5 to about 1.0.

13. The method of claim 1 , wherein the one or more structural characteristics comprise a number of endoneurial tubes per area.

14. The method of claim 1 , wherein the one or more structural characteristics comprise a percent of endoneurial tube lumen per area.

15. The method of claim 1 , wherein the one or more structural characteristics comprise a total perimeter of endoneurial tube lumens per area.

16. The method of claim 1 , wherein the determined one or more structural characteristics includes more than one structural characteristic, and at least one structural characteristic, of the more than one structural characteristics, is weighted, and
wherein the assessing the quality of the nerve graft is based on the more than one structural characteristics, including the at least one structural characteristic that has been weighted.

17. The method of claim 1 , wherein, before creating the transformed image, the method further comprises selecting one or more substructures in the obtained image.

18. The method of claim 17 , further comprising expressing one or more of the one or more structural characteristics per substructure, as a ratio of substructure area, or both.

19. The method of claim 1 , wherein the laminin-containing tissue is identified by one or more structural demarcation techniques.

20. The method of claim 19 , wherein the one or more structural demarcation techniques includes at least immunohistochemical staining.

21. A method for assessing the quality of a nerve graft, the method comprising:
obtaining an image of laminin-containing tissue in the nerve graft;
creating a transformed image using a transformation function of an image processing application on the image;
using an analysis function of the image processing application, analyzing the transformed image to identify one or more structures based on one or more recognition criteria;
determining one or more structural characteristics of the nerve graft derived from a measurement of one or more structures of interest determined based on the identified one or more structures;
comparing the one or more structural characteristics to one or more of:
a qualitative assessment score;
one or more reference ranges indicating an acceptable structural characteristic of the nerve graft; or
a bioassay result of the nerve graft; and

assessing the quality of the nerve graft based, at least in part, on one or more of the determined one or more structural characteristics of the nerve graft.

22. The method of claim 21 , wherein the identified one or more structures are one or more structures that are not of interest.

23. The method of claim 22 , wherein the identified one or more structures meet the one or more recognition criteria.

24. The method of claim 22 , wherein the identified one or more structures do not meet the one or more recognition criteria.

25. The method of claim 21 , wherein the identified one or more structures are the one or more structures of interest.

26. The method of claim 25 , wherein the identified one or more structures meet the one or more recognition criteria.

27. The method of claim 25 , wherein the identified one or more structures do not meet the one or more recognition criteria.

28. The method of claim 21 , wherein, before creating the transformed image, the method further comprises selecting one or more of an area of interest and/or a sampling window to delineate a selected image area, wherein the analyzing of the transformed image is performed only on the selected image area.

29. The method of claim 28 , wherein the area of interest comprises a nerve fascicle.

30. The method of claim 21 , wherein the one or more structures of interest is an endoneurial tube.

31. The method of claim 21 , wherein creating the transformed image comprises applying thresholding to the image.

32. The method of claim 31 , wherein applying the thresholding comprises applying one or more of a threshold method, a threshold color, a color space, and a dark background.

33. The method of claim 21 , wherein the one or more recognition criteria comprises a size range of the one or more structures.

34. The method of claim 33 , wherein the size range is from about 4.84 microns in diameter to about 16 microns in diameter.

35. The method of claim 21 , wherein the one or more recognition criteria comprise a circularity range of the one or more structures.

36. The method of claim 35 , wherein the circularity range is from about 0.5 to about 1.0.

37. The method of claim 21 , wherein the one or more structural ch

Assignees

• Axogen Corp

Inventors

• Deister Curt

Classifications

• G06V20/698 Primary

  Matching; Classification · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title

• G06T7/62 Primary

  of area, perimeter, diameter or volume · CPC title

• G06V20/695 Primary

  Preprocessing, e.g. image segmentation · CPC title

• G06T2207/10056

  Microscopic image · CPC title