Systems and methods for analytical comparison and monitoring of aneurysms
Systems and methods for identifying growth of a cerebral aneurysm. The method includes forming a first virtual skeleton model from a first image segmentation and forming a second virtual skeleton model from a second image segmentation. The virtual skeleton models including a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of a plurality of blood vessel surface points. The method includes identifying one or more second terminal points within the second virtual skeleton model and overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.
1. A method for identifying growth of a cerebral aneurysm, the method comprising:
providing a first image segmentation of a surface of a blood vessel network, the first image segmentation comprising a first plurality of blood vessel surface points;
forming a first virtual skeleton model from the first image segmentation, the first virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identifying one or more first nodes within the first virtual skeleton model, each of the one or more first nodes intersecting more than two edges of the plurality of edges;
providing a second image segmentation of a surface of the blood vessel network, the second image segmentation comprising a second plurality of blood vessel surface points;
forming a second virtual skeleton model from the second image segmentation, the second virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identifying one or more second nodes within the second virtual skeleton model, each of the one or more second nodes intersecting more than two edges of the plurality of edges; and
overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first nodes with the one or more second nodes.
2. The method of claim 1 , further comprising comparing geometry of the cerebral aneurysm when the first virtual skeleton model is overlapped with the second virtual skeleton model.
3. The method of claim 1 , further comprising:
identifying one or more first terminal points within the first virtual skeleton model, each of the one or more first terminal points intersecting only one edge of the plurality of edges;
identifying one or more second terminal points within the second virtual skeleton model, each of the one or more second terminal points intersecting only one edge of the plurality of edges; and
overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.
4. The method of claim 1 , wherein the one or more first nodes is a bifurcation point.
5. The method of claim 1 , wherein the one or more first nodes is part of a cycle.
6. A system for identifying growth of a cerebral aneurysm, the system comprising:
One or more processors configured to:
provide a first image segmentation of a surface of a blood vessel network, the first image segmentation comprising a first plurality of blood vessel surface points;
form a first virtual skeleton model from the first image segmentation, the first virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identify one or more first nodes within the first virtual skeleton model, each of the one or more first nodes intersecting more than two edges of the plurality of edges;
provide a second image segmentation of a surface of the blood vessel network, the second image segmentation comprising a second plurality of blood vessel surface points;
form a second virtual skeleton model from the second image segmentation, the second virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identify one or more second nodes within the second virtual skeleton model, each of the one or more second nodes intersecting more than two edges of the plurality of edges; and
overlap the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first nodes with the one or more second nodes.
7. The system of claim 6 , wherein the one or more processors are further configured to compare geometry of the cerebral aneurysm when the first virtual skeleton model is overlapped with the second virtual skeleton model.
8. The system of claim 6 , wherein the one or more processors are further configured to:
identify one or more first terminal points within the first virtual skeleton model, each of the one or more first terminal points intersecting only one edge of the plurality of edges;
identify one or more second terminal points within the second virtual skeleton model, each of the one or more second terminal points intersecting only one edge of the plurality of edges; and
overlap the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.
9. The system of claim 6 , wherein the one or more first nodes is a bifurcation point.
10. The system of claim 6 , wherein the one or more first nodes is part of a cycle.
11. The system of claim 6 , wherein the one or more second nodes is a bifurcation point.
12. The system of claim 6 , wherein the one or more second nodes is part of a cycle.
13. A method for identifying growth of a cerebral aneurysm, the method comprising:
providing a first image segmentation of a surface of a blood vessel network, the first image segmentation comprising a first plurality of blood vessel surface points;
forming a first virtual skeleton model from the first image segmentation, the first virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identifying one or more first terminal points within the first virtual skeleton model, each of the one or more first terminal points intersecting only one edge of the plurality of edges;
providing a second image segmentation of a surface of the blood vessel network, the second image segmentation comprising a second plurality of blood vessel surface points;
forming a second virtual skeleton model from the second image segmentation, the second virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;
identifying one or more second terminal points within the second virtual skeleton model, each of the one or more second terminal points intersecting only one edge of the plurality of edges; and
overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.
14. The method of claim 13 , further comprising comparing geometry of the cerebral aneurysm when the first virtual skeleton model is overlapped with the second virtual skeleton model.
15. The method of claim 13 , further comprising:
identifying one or more first nodes within the first virtual skeleton model, each of the one or more first nodes intersecting more than one edge of the plurality of edges;
identifying one or more second nodes within the second virtual skeleton model, each of the one or more second nodes intersecting more than one edge of the plurality of edges; and
overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first nodes with the one or more second nodes.
16. The method of claim 15 , wherein the one or more first nodes is a bifurcation point.
17. The method of claim 15 , wherein the one or more first nodes is part of a cycle.
18. The method of claim 15 , wherein the one or more second nodes is a bifurcation point.
19. The method of claim 15 , wherein the one or more second nodes is part of a cycle.
20. The method of claim 19 , wherein the one or more first nodes is a vertex.