Systems and methods for accelerated online adaptive radiation therapy

1. A method for validating an accuracy of radiation treatment plan contour data, the method comprising:

(a) accessing with a computer system, previously generated contour data comprising contours associated with a radiation treatment plan for a subject;

(b) accessing with the computer system, an image of the subject;

(c) generating with the computer system, inner shell data by eroding contours in the contour data by a first margin;

(d) generating with the computer system, outer shell data by expanding contours in the contour data by a second margin;

(e) computing with the computer system, image feature data from the image;

(f) generating with the computer system, an inner shell image feature map from the image feature data over contours in the inner shell data;

(g) generating with the computer system, an outer shell image feature map from the image feature data over contours in the outer shell data;

(h) validating the contour data by inputting the inner shell image feature map and the outer shell image feature map to a machine learning algorithm, generating output as labeled contour data, wherein the labeled contour data labels contour data that are validated as accurate contour data contour data that are not validated as inaccurate contour data;

(i) generating a binary mask for a given contour in the contour data by assigning a first binary value to pixels within the contour and assigning a second binary value to pixels outside the contour, and wherein the inner shell data are generated by eroding the binary mask by the first margin and the outer shell data are generated by dilating the binary mask by the second margin; and

(j) generating core region data from the binary mask, wherein the core region data are generated by:

generating a distance map by computing a distance metric between each pixel in the binary mask and a boundary pixel nearest to that pixel in the binary mask; and

defining the core region based on a percent of pixels having highest distance metric values in the distance map.

2. The method as recited in claim 1 , wherein the distance metric is a Euclidean distance metric.

3. The method as recited in claim 1 , wherein the percent is 20 percent.

4. The method as recited in claim 1 , wherein the percent is 30 percent.

5. The method as recited in claim 1 , further comprising generating with the computer system, a core region image feature map from the image feature data over the outer core region.

6. The method as recited in claim 5 , wherein the machine learning algorithm implements a decision tree model that takes as inputs the core region image feature data, the inner shell image feature data, and the outer shell image feature data.

7. The method as recited in claim 6 , wherein the decision tree model comprises:

a first level in which the core region image feature data are evaluated relative to ground truth data;

a second level in which the inner shell image feature data are evaluated relative to ground truth data; and

a third level in which the outer shell image feature data are evaluated relative to ground truth data.

8. The method as recited in claim 7 , wherein the first level of the decision tree model evaluates a selected set of image features in the core region image feature data to similar image features in the ground truth data.

9. The method as recited in claim 8 , wherein the set of image features is selected based on a hierarchical clustering of the image feature data contained in the core region.

10. The method as recited in claim 7 , wherein a contour is validated when its corresponding image feature data pass the first level, the second level, and the third level of the decision tree model.

11. The method as recited in claim 1 , wherein the inner shell image feature map and the outer shell image feature map are each normalized based on image feature data contained in the core region.

12. The method as recited in claim 1 , wherein the first margin and the second margin have a same margin value.

13. The method as recited in claim 12 , wherein the margin value is 4 mm.

14. The method as recited in claim 1 , wherein the image feature data comprise intensity histogram-based image features.

15. The method as recited in claim 14 , wherein the intensity histogram-based image features comprise at least one of mean, standard deviation, range, skewness, and kurtosis.

16. The method as recited in claim 1 , wherein the image feature data comprise texture features computed based on a gray-level co-occurrence matrix.

17. The method as recited in claim 1 , wherein the image feature data comprise texture features computed based on a gray-level run-length matrix.

18. The method as recited in claim 1 , wherein the machine learning algorithm implements a recursive random forest classification to select a set of image features against which the inner shell image feature map and the outer shell image feature map are compared to validate the contour data.

19. The method as recited in claim 1 , further comprising:

inputting the inaccurate contour data in the labeled contour data to a contour correction algorithm, generating output as corrected contour data.

20. The method as recited in claim 19 , wherein the corrected contour data are further validated by repeating steps (c)-(h) using the corrected contour data as input contour data.

21. The method as recited in claim 19 , wherein the contour correction algorithm comprises:

computing with the computer system, second image feature data for a region-of-interest (ROI) in the image, wherein the ROI is defined by a contour in the inaccurate contour data;

generating with the computer system, an image feature map from the second image feature data, wherein the image feature map depicts image feature values in the ROI;

generating corrected contour data by inputting the image feature map and the inaccurate contour data to an active contour algorithm, generating output as the corrected contour data;

wherein the image feature map is used by the active contour algorithm as an external force that guides an active curve to find a corrected boundary for the inaccurate contour data.

22. A method for validating an accuracy of radiation treatment plan contour data, the method comprising:

(a) accessing with a computer system, previously generated contour data comprising contours associated with a radiation treatment plan for a subject;

(b) accessing with the computer system, an image of the subject;

(c) generating with the computer system, inner shell data by eroding contours in the contour data by a first margin;

(d) generating with the computer system, outer shell data by expanding contours in the contour data by a second margin, wherein the first margin and the second margin have a same margin value;

(e) computing with the computer system, image feature data from the image;

(f) generating with the computer system, an inner shell image feature map from the image feature data over contours in the inner shell data;

(g) generating with the computer system, an outer shell image feature map from the image feature data over contours in the outer shell data; and

(h) validating the contour data by inputting the inner shell image feature map and the outer shell image feature map to a machine learning algorithm, generating output as labeled contour data, wherein the labeled contour data labels contour data that are validated as accurate contour data contour data that are not validated as inaccurate contour data.

23. The method as recited in claim 22 , wherein the margin value is 4 mm.

24. A method for validating an accuracy of radiation treatment plan contour data, the method comprising:

(a) accessing with a computer system, previously generated contour data comprising contours associated with a radiation treatment plan for a subject;

(b) accessing with the computer system, an image of the subject;

(c) generating with the computer system, inner shell data by eroding contours in the contour data by a first margin;

(d) generating with the computer system, outer shell data by expanding contours in the contour data by a second margin;

(e) computing with the computer system, image feature data from the image;

(f) generating with the computer system, an inner shell image feature map from the image feature data over contours in the inner shell data;

(g) generating with the computer system, an outer shell image feature map from the image feature data over contours in the outer shell data; and

(h) validating the contour data by inputting the inner shell image feature map and the outer shell image feature map to a machine learning algorithm, generating output as labeled contour data, wherein the labeled contour data labels contour data that are validated as accurate contour data contour data that are not validated as inaccurate contour data;

wherein the machine learning algorithm implements a recursive random forest classification to select a set of image features against which the inner shell image feature map and the outer shell image feature map are compared to validate the contour data.

25. A method for validating an accuracy of radiation treatment plan contour data, the method comprising:

(a) accessing with a computer system, previously generated contour data comprising contours associated with a radiation treatment plan for a subject;

(b) accessing with the computer system, an image of the subject;

(c) generating with the computer system, inner shell data by eroding contours in the contour data by a first margin;

(d) generating with the computer system, outer shell data by expanding contours in the contour data by a second margin;

(e) computing with the computer system, image feature data from the image;

(f) generating with the computer system, an inner shell image feature map from the image feature data over contours in the inner shell data;

(g) generating with the computer system, an outer shell image feature map from the image feature data over contours in the outer shell data;

(h) validating the contour data by inputting the inner shell image feature map and the outer shell image feature map to a machine learning algorithm, generating output as labeled contour data, wherein the labeled contour data labels contour data that are validated as accurate contour data contour data that are not validated as inaccurate contour data; and

(i) inputting the inaccurate contour data in the labeled contour data to a contour correction algorithm, generating output as corrected contour data.

26. The method as recited in claim 25 , wherein the corrected contour data are further validated by repeating steps (c)-(h) using the corrected contour data as input contour data.

27. The method as recited in claim 25 , wherein the contour correction algorithm comprises:

computing with the computer system, second image feature data for a region-of-interest (ROI) in the image, wherein the ROI is defined by a contour in the inaccurate contour data;

generating with the computer system, an image feature map from the second image feature data, wherein the image feature map depicts image feature values in the ROI;

generating corrected contour data by inputting the image feature map and the inaccurate contour data to an active contour algorithm, generating output as the corrected contour data;

wherein the image feature map is used by the active contour algorithm as an external force that guides an active curve to find a corrected boundary for the inaccurate contour data.