Adaptive auto-segmentation in computed tomography

Patent metadata

Field	Value
Publication number	US-12229963-B2
Application number	US-202117546080-A
Country	US
Kind code	B2
Filing date	Dec 9, 2021
Priority date	Sep 29, 2021
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

A computer-implemented method of segmenting a reconstructed volume of a region of patient anatomy includes: determining an anatomical region associated with the reconstructed volume; detecting one or more metal objects disposed in an initial 3D metal object mask associated with the reconstructed volume; for each of the one or more metal objects disposed in the initial 3D metal object mask, determining a volume associated with the metal object; determining a value for at least one segmentation parameter based on the anatomical region and on the volume associated with the one or more metal objects; and generating a final 3D metal object mask associated with the reconstructed digital volume using the value for the segmentation parameter.

First claim

We claim:

1. A computer-implemented method of segmenting a reconstructed volume of a region of patient anatomy, the method comprising:
determining an anatomical region associated with the reconstructed volume;
detecting one or more metal objects disposed in an initial 3D metal object mask associated with the reconstructed volume;
for each of the one or more metal objects disposed in the initial 3D metal object mask, determining a volume associated with the metal object;
determining a value for at least one segmentation parameter based on the anatomical region and on the volume associated with the one or more metal objects; and
generating a final 3D metal object mask associated with the reconstructed volume using the value for the segmentation parameter.

2. The computer-implemented method of claim 1 , wherein the at least one segmentation parameter comprises a thresholding parameter or a dilation radius.

3. The computer-implemented method of claim 1 , further comprising:
for each of the one or more metal objects disposed in the initial 3D metal object mask, determining a radiographic density associated with each metal object; and
determining the value for the at least one segmentation parameter based on the radiographic density associated with each metal object.

4. The computer-implemented method of claim 1 , wherein determining the anatomical region comprises generating an anatomical mask for the reconstructed volume.

5. The computer-implemented method of claim 4 , wherein generating the anatomical mask for the reconstructed volume comprises including locations in the anatomical mask based on an anatomical threshold value.

6. The computer-implemented method of claim 5 , wherein the anatomical threshold value is associated with a material having a radiodensity that is greater than that of air.

7. The computer-implemented method of claim 5 , wherein the anatomical threshold value is associated with a material having a radiodensity that is less than that of water.

8. The computer-implemented method of claim 1 , wherein determining the anatomical region associated with the reconstructed volume comprises determining whether the anatomical region is a head region or a body region based on an anatomical mask for the reconstructed volume.

9. The computer-implemented method of claim 8 , wherein determining whether the anatomical region is the head region or the body region based on the anatomical mask comprises determining a minimum lateral width of the anatomical mask.

10. The computer-implemented method of claim 9 , wherein determining the minimum lateral width of the anatomical mask is based on a maximum lateral width for each slice of the anatomical mask that has a non-zero width.

11. The computer-implemented method of claim 1 , wherein detecting the one or more metal objects disposed in the initial 3D metal object mask associated with the reconstructed volume comprises:
generating the initial 3D metal object mask for the reconstructed volume; and
detecting one or more connected components contained within the initial metal object mask.

12. The computer-implemented method of claim 11 , wherein detecting the one or more metal objects disposed in the initial 3D metal object mask associated with the reconstructed volume further comprises determining a respective volume for each of the one or more connected components contained within the initial metal object mask.

13. The computer-implemented method of claim 11 , wherein detecting the one or more metal objects disposed in the initial 3D metal object mask associated with the reconstructed volume further comprises determining a metal classification of the reconstructed volume.

14. The computer-implemented method of claim 13 , wherein the metal classification of the reconstructed volume is selected from the group consisting of a metal-free anatomical region, a dental region, a fiducial-containing region, and an orthopedic region.

15. The computer-implemented method of claim 13 , wherein determining the metal classification of the reconstructed volume is based on at least one of a volume of a largest component of the one or more connected components, a cumulative volume of a set of the one or more connected components that are larger than a predetermined volume, or a radiographic density of at least one of the one or more connected components that are larger than a predetermined volume.

16. The computer-implemented method of claim 11 , wherein generating the initial 3D metal object mask for the reconstructed volume comprises determining locations in the initial 3D metal object mask based on an initial metal threshold value.

17. The computer-implemented method of claim 1 , further comprising, generating a set of 2D projection metal masks by performing a forward projection process on the final 3D metal object mask associated with the reconstructed volume.

18. The computer-implemented method of claim 17 , wherein each 2D mask projection in the set of 2D mask projections includes location information indicating pixels that are blocked during the forward projection process by one or more connected components contained within the initial metal object mask.

19. The computer-implemented method of claim 1 , further comprising:
generating a non-binary mask based on the final 3D metal object mask and the reconstructed volume; and
generating a final reconstructed volume based on the non-binary mask, the reconstructed volume, and a low-artifact reconstructed volume.

20. The computer-implemented method of claim 19 , wherein generating the final reconstructed volume based on the non-binary mask comprises, for each edge voxel of a metal object in the reconstructed volume, providing a value that is based on an image value from the reconstructed volume, an image value from the low-artifact reconstructed volume, and a value from the non-binary mask.

Assignees

• Siemens Healthineers Int Ag

Inventors

• Peterlik Igor
• Strzelecki Adam Michal
• Seghers Dieter Marc
• Plamondon Mathieu
• Lehmann Mathias
• Paysan Pascal
• Heinz Alexander

Classifications

• G06T12/10 Primary

  Image preprocessing, e.g. calibration, positioning of sources or scatter correction · CPC title

• G06T2207/30196

  Human being; Person · CPC title

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• A61N5/1067

  in real time, i.e. during treatment · CPC title

• A61N5/1039

  using functional images, e.g. PET or MRI · CPC title