SYSTEM AND METHOD FOR AUTO-CONTOURING IN ADAPTIVE RADIOTHERAPY

• US 20150238158A1
• Filed: 02/27/2014
• Published: 08/27/2015
• Est. Priority Date: 02/27/2014
• Status: Active Grant

• Current Assignee: IMPAC Medical Systems, Inc. (Elekta AB)
• Original Assignee: IMPAC Medical Systems, Inc. (Elekta AB)
• Inventors: Han, Xiao, ZHOU, Yan, Magro, Nicolette Patricia
• Granted Patent: US 9,629,598 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 6/5205 involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30081 Prostate
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/73 using feature-based methods

Abstract

A method for performing automatic contouring in a medical image. The method may include receiving an image containing a region of interest and determining a first contour of the region of interest using a boundary detector. The method may include refining the first contour based on a shape dictionary to generate a second contour of the region of interest and updating at least one of the boundary detector or the shape dictionary based on the second contour.

Claims

1. A method, implemented by a processor, for performing auto-contouring in a medical image, the method comprising:

receiving an image containing a region of interest;

determining, by the processor, a first contour of the region of interest using a boundary detector;

refining, by the processor, the first contour based on a shape dictionary to generate a second contour of the region of interest; and

updating at least one of the boundary detector or the shape dictionary based on the second contour.

2. The method of claim 1, comprising refining, by the processor, the second contour using the boundary detector.

3. The method of claim 1, wherein determining, by the processor, the first contour includes:

determining whether an image point of the image is on a boundary of the region of interest; and

selecting a set of image points that are determined to be on the boundary of the region of interest to form the first contour.

4. The method of claim 3, wherein:

determining whether the image point of the image is on the boundary of the region of interest includes determining a probability of the image point being on the boundary; and

selecting the set of image points to form the first contour includes selecting image points having probabilities higher than a predetermined threshold.

5. The method of claim 1, wherein refining, by the processor, the first contour based on the shape dictionary to generate the second contour includes:

selecting a set of shapes from the shape dictionary;

combining the selected set of shapes to approximate the first contour; and

generating the second contour based on the combined set of shapes.

6. The method of claim 5, including:

determining whether at least a subset of image points on the first contour are on the second contour by minimizing an optimization function.

7. The method of claim 1, wherein updating at least one of the boundary detector or the shape dictionary based on the second contour includes updating the boundary detector, and

(a) after updating the boundary detector based on the second contour, determining an updated first contour;

(b) refining the updated first contour based on the shape dictionary to generate an updated second contour; and

repeating steps (a) and (b) until at least one of the following conditions is met;

a number of repetitions reaches a predetermined value;

or

a residual error reaches a predetermine minimum.

8. The method of claim 1, wherein updating at least one of the boundary detector or the shape dictionary based on the second contour includes updating the boundary detector, wherein updating the boundary detector includes:

comparing the image having the second contour with a collection of images containing the region of interest;

selecting a subset of images from the collection similar to the image having the second contour; and

updating the boundary detector based on the image having the second contour and the selected subset of images.

9. The method of claim 8, wherein updating the boundary detector based on the image having the second contour and the selected subset of images includes:

segregating positive and negative image points;

selecting positive image points located on the second contour or on boundaries of the selected subset of images;

extracting steerable features from the selected positive image points;

determining a feature vector based on the extracted steerable features; and

updating the boundary detector based on the feature vector.

10. The method of claim 1, wherein updating at least one of the boundary detector or the shape dictionary based on the second contour includes updating the shape dictionary, and wherein updating the shape dictionary includes:

selecting a subset of shapes from the image containing the second contour;

obtaining a sparse coefficient for each shape in the subset; and

updating the shape dictionary based on the obtained sparse coefficients.

11. A system for performing auto-contouring in a medical image, comprising:

a processor;

a memory operatively coupled to the processor and storing instructions, that when executed by the processor, causes the processor to perform a method, the method comprising;

receiving an image containing a region of interest;

determining a first contour of the region of interest using a boundary detector;

refining the first contour based on a shape dictionary to generate a second contour of the region of interest; and

updating at least one of the boundary detector or the shape dictionary based on the second contour.

12. The system of claim 11, wherein determining the first contour of the region of interest using a boundary detector includes:

determining whether an image point of the image is on a boundary of the region of interest; and

selecting a set of image points that are determined to be on the boundary of the region of interest to form the first contour.

13. The system of claim 12, wherein determining whether an image point of the image is on a boundary of the region of interest includes determining a probability of the image point being on the boundary;

and selecting image points having probabilities higher than a predetermined threshold.

14. The system of claim 11, wherein refining the first contour based on the shape dictionary to generate the second contour of the region of interest includes:

selecting a set of shapes from the shape dictionary;

combining the selected set of shapes to approximate the first contour; and

generating the second contour based on the combined set of shapes.

15. The system of claim 14, wherein the method includes:

determining whether at least a subset of image points on the first contour are on the second contour by minimizing an optimization function.

16. The system of claim 11, wherein updating at least one of the boundary detector or the shape dictionary based on the second contour includes updating the boundary detector, and

(a) after updating the boundary detector based on the second contour, determining an updated first contour;

(b) refining the updated first contour based on the shape dictionary to generate an updated second contour; and

repeating steps (a) and (b) until at least one of the following conditions is met;

a number of repetitions reaches a predetermined value;

or

a residual error reaches a predetermine minimum.

17. The system of claim 11, wherein the method includes refining the second contour using the boundary detector.

18. A computer-implemented method for training a boundary detector based on a plurality of medical images containing a region of interest, the method comprising:

selecting, by a processor, image points located on boundaries of the region of interest from the plurality of medical images;

extracting, by the processor, steerable features from the selected image points;

determining, by the processor, a feature vector based on the extracted steerable features; and

generating, by the processor, the boundary detector based on the feature vector.

19. The computer-implemented method of claim 18, comprising:

comparing a new image having a new contour with the plurality of medical images;

selecting a subset of medical images from the plurality based on their similarities to the new image having the new contour; and

updating the boundary detector based on the new image having the new contour and the selected subset of medical images.

20. The computer-implemented method of claim 19, comprising:

selecting a subset of shapes from the new image containing the new contour;

obtaining a sparse coefficient for each shape in the subset; and

updating a shape dictionary based on the obtained sparse coefficient.