Shape similarity measure for body tissue

• US 10,186,031 B2
• Filed: 11/30/2016
• Issued: 01/22/2019
• Est. Priority Date: 06/20/2014
• Status: Active Grant

• Current Assignee: Varian Medical Systems International AG (Siemens AG)
• Original Assignee: Varian Medical Systems International AG (Siemens AG)
• Inventors: Coradi, Thomas, Haas, Benjamin
• Primary Examiner(s): Yentrapati, Avinash
• Application Number:
  US15/365,537
• Publication Number:
  US 20170084030A1
• Time in Patent Office: 783 Days
• Field of Search: None
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20124 Active shape model [ASM]
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06V 10/752 Contour matching
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures

Abstract

A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Claims

1. A method of determining an accuracy of shape-determining processes for identifying shapes of a body tissue in one or more images of a patient, the method comprising:

receiving first data that defines a first boundary of a first shape of the body tissue in at least a portion of the one or more images, the first shape being determined by a first shape-determining process;

receiving second data that defines a second boundary of a second shape of the body tissue in at least a portion of the one or more images, the second shape being determined by a second shape-determining process;

determining, by a computer system, an intersection shape of the first shape and the second shape;

determining, by the computer system, a union shape of the first shape and the second shape;

calculating, by the computer system, a difference between the union shape and the intersection shape;

computing, by the computer system, a shape similarity metric based on the difference; and

providing the shape similarity metric for determining an accuracy of the second shape-determining process relative to the first shape-determining process.

2. The method of claim 1, wherein the first data and the second data includes the one or more images of the patient, and wherein the method further comprises:

determining a reference point in the one or more images, wherein each of the one or more images include the reference point; and

defining coordinates of the first shape and the second shape with respect to the reference point.

3. The method of claim 2, wherein calculating the difference between the union shape and the intersection shape includes:

for each of a plurality of points on the intersection shape;

identifying a corresponding point on the union shape that corresponds to the point on the intersection shape; and

calculating a distance between the point on the intersection shape and the corresponding point on the union shape; and

computing an average of the distances to obtain the difference.

4. The method of claim 1, wherein calculating the difference between the union shape and the intersection shape includes:

computing an intersection size of the intersection shape;

computing a union size of the union shape; and

subtracting the intersection size from the union size to obtain the difference.

5. The method of claim 4, wherein computing the shape similarity metric includes:

calculating a normalization factor; and

multiplying the difference and the normalization factor to obtain the shape similarity metric.

6. The method of claim 5, wherein the normalization factor is determined based on a property of the first shape and the second shape.

7. The method of claim 5, wherein computing the intersection size includes taking a square root of an intersection area of the intersection shape, wherein computing the union size includes taking the square root of a union area of the union shape, and wherein the normalization factor includes a sum of:

a first term including the square root of a first area of the first shape divided by a first circumference of the first shape; and

a second term including the square root of a second area of the second shape divided by a second circumference of the second shape.

8. The method of claim 5, wherein the first shape is a first two-dimensional slice of the body tissue, wherein the second shape is a second two-dimensional slice of the body tissue, the method further comprising:

computing shape similarity metrics for other slices of the body tissue; and

combining the shape similarity metrics for the slices to obtain a total shape similarity metric.

9. The method of claim 5, wherein the first shape and the second shape are three-dimensional, wherein computing the intersection size includes taking a cube root of an intersection volume of the intersection shape, wherein computing the union size includes taking the cube root of a union volume of the union shape, and wherein the normalization factor is

10. A method of determining an accuracy of shape-determining processes for identifying shapes of a body tissue in one or more images of a patient, the method comprising:

receiving first data that defines a first boundary of a first shape of the body tissue in at least a portion of the one or more images, the first shape being determined by a first shape-determining process;

determining, by a computer system, that the first shape does not intersect with a second shape; and

computing, by the computer system, a shape similarity metric by;

determining a first size of the first shape, the first size determined using a first area or a first volume of the first shape and a first normalization factor for the first shape; and

using the first size to compute the shape similarity metric.

11. The method of claim 10, further comprising:

receiving second data that defines a second boundary of the second shape of the body tissue, the second shape being determined by a second shape-determining process;

wherein computing the shape similarity metric further includes;

determining a second size of the second shape, the second size determined using a second area or a second volume of the second shape and a second normalization factor for the second shape; and

calculating a sum including the first size and the second size.

12. The method of claim 11, wherein:

the first shape and the second shape are two-dimensional and the first normalization factor of the first shape includes an inverse of a circumference of the first shape, or

the first shape and the second shape are three-dimensional and the first normalization factor of the first shape includes the inverse of a surface area of the first shape.

13. The method of claim 10, wherein a corresponding second shape does not exist, and wherein:

the first shape is two-dimensional and the shape similarity metric is

14. The method of claim 10, further comprising:

comparing the shape similarity metric for the first shape and the second shape to another shape similarity metric of a different pair of shapes.

15. A method of comparing shapes of body tissues in images of patients, the method comprising:

receiving first data that defines a first boundary of a first shape of a first body tissue of a first patient;

for each of a plurality of second patients;

receiving second data that defines a second boundary of a second shape of a second body tissue of one of the plurality of second patients;

determining, by a computer system, an intersection shape of the first shape and the second shape;

determining, by the computer system, a union shape of the first shape and the second shape;

calculating, by the computer system, a difference between the union shape and the intersection shape; and

computing, by the computer system, a shape similarity metric based on the difference; and

identifying a first shape similarity metric that satisfies one or more criteria, the first shape similarity metric corresponding to a particular second patient.

16. The method of claim 15, wherein the first body tissue is a tumor, wherein treatment information for the particular second patient is retrieved, and wherein the treatment information includes one or more of:

a geometry of a radiation beam, a dosage of the radiation beam, and a number of sessions for treatment using the radiation beam.

17. The method of claim 15, further comprising:

aligning the first shape and the second shape in a coordinate space.

18. The method of claim 15, wherein the one or more criteria specifies a highest shape similarity metric is to be selected.

19. The method of claim 15, wherein identifying the first shape similarity metric that satisfies the one or more criteria includes:

identifying a set of the shape similarity metrics of the plurality of second patients that satisfy the one or more criteria; and

selecting the first shape similarity metric from the set of the shape similarity metrics.

20. The method of claim 19, wherein the one or more criteria include a threshold value for the shape similarity metrics.

21. The method of claim 19, wherein selecting the first shape similarity metric from the set of the shape similarity metrics includes:

identifying the particular second patient corresponding to the first shape similarity metric based on treatment outcomes for the second patients whose shape similarity metrics satisfy the one or more criteria.

22. The method of claim 16, further comprising:

determining a treatment plan for the first patient based on the treatment information.

23. The method of claim 22, further comprising:

performing the treatment plan using a radiation beam.

24. The method of claim 15, wherein the first shape and the second shapes are determined using a same shape-determining process.

25. A computer product comprising a non-transitory computer readable medium storing a plurality of instructions that when executed control a computer system to determine an accuracy of shape-determining processes for identifying shapes of a body tissue in one or more images of a patient, the instructions comprising:

receiving first data that defines a first boundary of a first shape of the body tissue, the first shape being determined by a first shape-determining process;

receiving second data that defines a second boundary of a second shape of the body tissue, the second shape being determined by a second shape-determining process;

determining an intersection shape of the first shape and the second shape;

determining a union shape of the first shape and the second shape;

calculating a difference between the union shape and the intersection shape;

computing a shape similarity metric based on the difference; and

providing the shape similarity metric for determining an accuracy of the second shape-determining process relative to the first shape-determining process.