Vessel centerline determination
A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.
1. A method of centerline determination for a tubular tissue in a digital medical image data set defined in a digital data space, comprising:
receiving coordinates of at least one start point and one end point inside a tubular tissue volume of the digital data space;
automatically determining a path between said points that remains inside said volume, comprising using targeted marching which uses a cost function incorporating both path cost and estimated future path cost;
automatically segmenting said tubular tissue using said path; and
automatically determining a centerline for said tubular tissue from said segmentation,
wherein said receiving, said determining a path, said segmenting, and said determining a centerline are all performed on a same digital data space of said digital medical image data set.
2. A method according to claim 1 , wherein said tubular tissue comprises a body lumen.
3. A method according to claim 1 , wherein receiving comprises receiving at most 4 points from a human user.
4. A method according to claim 1 , wherein receiving comprises receiving at most 2 points from a human user.
5. A method accord to claim 1 , wherein determining a path comprises propagating a sub-path from each of at least two of said received points until the sub-paths meet.
6. A method accord to claim 1 , wherein determining a path comprises propagating a sub-path from one of said received points until it meets another of the received points.
7. A method according to claim 1 , wherein propagating a sub-path comprises selecting a point and selecting a neighbor of the selected point for further consideration responsive to said cost function.
8. A method according to claim 1 , wherein a path cost of a point is a function of a local cost of a point and a path cost of at least one neighbor of the point.
9. A method according to claim 8 , wherein a local cost of a point is a function of a probability of the point being inside or outside of the tubular tissue.
10. A method according to claim 8 , wherein a path cost is determined by attempting to find at least an approximate solution to an equation including at least one extreme-type function that returns an extreme value of its operands.
11. A method according to claim 10 , wherein if a solution is not found, at least one of said extreme-type functions is replaced by a constant value.
12. A method according to claim 11 , said extreme-type function to replace is found by a min-max method.
13. A method according to claim 10 , wherein said equation includes an approximation of a gradient of the path cost.
14. A method according to claim 9 , wherein said probability is determined using a histogram of data point values.
15. A method according to claim 14 , comprising updating the histogram when a point is determined to be inside or outside of the tubular tissue.
16. A method according to claim 14 , comprising updating the histogram when a point is selected.
17. A method according to claim 16 , wherein said histogram is updated with a weight corresponding to a probability of the point being inside the tubular tissue.
18. A method according to claim 14 , comprising generating a local histogram for a part of said tubular tissue.
19. A method according to claim 14 , wherein the histogram comprises an outside histogram for point values that are outside the tubular tissue.
20. A method according to claim 19 , wherein the outside histogram includes also points inside the tubular tissue.
21. A method according to claim 14 , wherein the histogram comprises an inside histogram for point values that are inside the tubular tissue.
22. A method according to claim 1 , wherein a path cost of a point is a function of a probability of the point being inside or outside of the tubular tissue.
23. A method according to claim 1 , comprising selecting a target to be used in an estimating of said future cost.
24. A method according to claim 23 , wherein said estimating is an underestimating.
25. A method according to claim 23 , wherein said estimating is based on an average cost per distance unit.
26. A method according to claim 23 , wherein said estimating is based on an Euclidian distance to said target.
27. A method according to claim 23 , wherein selecting a target comprises selecting from two or more possible targets.
28. A method according to claim 27 , wherein selecting a target comprises projecting two vectors, one for each of two potential targets on a vector connecting a current point with a starting point of the current point and selecting a longer projection.
29. A method according to claim 23 , wherein selecting a target comprises selecting one of said received points.
30. A method according to claim 1 , comprising correcting said determined path.
31. A method according to claim 30 , wherein correcting said path comprising interconnecting path segments.
32. A method according to claim 1 , wherein said segmenting uses a marching method for segmentation.
33. A method according to claim 32 , wherein said marching method assigns a value for each point in said tubular tissue.
34. A method according to claim 32 , wherein said marching method is a fast marching method.
35. A method according to claim 1 , wherein said segmenting uses a contour expansion method.
36. A method according to claim 1 , wherein said segmenting comprises generating a parameterization for points along said path.
37. A method according to claim 36 , comprising propagating said parameterization.
38. A method according to claim 37 , wherein said propagated parameterization is used to prevent leakage of said segmentation.
39. A method according to claim 38 , comprising collecting propagation statistics for different parameterization values.
40. A method according to claim 39 , comprising limiting propagation of at least one parameterization value based on said statistics.
41. A method according to claim 40 , wherein limiting comprises limiting propagation to be substantially locally uniform for nearby parameterizations.
42. A method according to claim 38 , comprising determining a direction of propagation from a propagation of parameterization values.
43. A method according to claim 37 , wherein said parameterization is propagated substantially parallel to said path.
44. A method according to claim 43 , comprising propagating said parameterization to being substantially perpendicular to a path cost gradient associated with said propagation.
45. A method according to claim 37 , comprising controlling a direction of propagation based on said parameterization.
46. A method according to claim 1 , wherein said segmenting comprises partitioning said path into portions.
47. A method according to claim 46 , comprising defining boundary planes between said portions.
48. A method according to claim 46 , wherein said portions overlap by a substantially small amount.
49. A method according to claim 46 , wherein said portions are substantially straight lines.
50. A method according to claim 46 , wherein said partitioning is used to reduce leakage of said segmentation.
51. A method according to claim 1 , wherein said segmenting comprises propagating from said path.
52. A method according to claim 51 , wherein said propagating is limited to be substantially perpendicular to said path.
53. A method according to claim 51 , wherein said propagating is limited to be substantially locally uniform in a radial direction.
54. A method according to claim 51 , wherein said propagating depends on a local curvature.
55. A method according to claim 54 , wherein said local curvature is estimated by counting visited neighbors.
56. A method according to claim 1 , wherein said segmenting comprises segmenting using a histogram of data values to determine a probability of a point being inside the tubular tissue.
57. A method according to claim 56 , wherein different parts along said path have different histograms.
58. A method according to claim 57 , wherein said histograms are created to vary smoothly between said parts.
59. A method according to claim 57 , wherein a noise level in at least one of said histograms is reduced using a global histogram.
60. A method according to claim 56 , comprising repeatedly updating said histograms during said segmenting.
61. A method according to claim 1 , comprising cleaning the segmentation.
62. A method according to claim 1 , wherein determining a centerline comprises generating a distance map of said tubular tissue, of distances from an outer boundary of said tubular tissue, inwards.
63. A method according to claim 62 , wherein generating a distance map comprises using morphological skeletonization on said segmentation.
64. A method according to claim 62 , wherein generating a distance map comprises using fast marching on said segmentation.
65. A method according to claim 62 , wherein determining a centerline comprises finding a path in said distance map.
66. A method according to claim 65 , wherein finding a path for said centerline comprises targeted marching from at least one end of said segmentation.
67. A method according to claim 66 , wherein said targeted marching for finding a path comprises taking a local curvature into account.
68. A method according to claim 1 , wherein said data set is three dimensional.
69. A method of centerline determination for a tubular tissue in a digital medical image data set defined in a digital data space, comprising:
receiving at least one start point and one end point inside a tubular tissue volume;
automatically determining a path between said points that remains inside said volume, comprising determining using targeted marching which uses a cost function incorporating both path cost and estimated future path cost, wherein a path cost of a point is a function of a probability of the point being inside or outside of the tubular tissue;
automatically segmenting said tubular tissue using said path; and
automatically determining a centerline for said tubular tissue from said segmentation,
wherein said receiving, said determining a path, said segmenting, and said determining a centerline are all performed on a same digital data space of said digital medical image data set.