Determining centerlines in elongated structures in images to detect abnormalities

1. A system for determining an abnormality in an elongated structure in a three dimensional medical image, the system comprising

an electronic processor configured to:

determine a centerline of the elongated structure in the three dimensional medical image;

determine a plurality of two dimensional cross sections of the three dimensional medical image based on the centerline; and

for each two dimensional cross section of the plurality of two dimensional cross sections;

convert the two dimensional cross section to polar coordinates;

fit a line to the elongated structure in the two dimensional cross section converted to polar coordinates; and

reconvert the two dimensional cross section to Cartesian coordinates.

2. The system according to claim 1 , wherein the electronic processor is further configured to:

determine a diameter of the elongated structure in the two dimensional cross section; and

when the diameter exceeds a predetermined threshold and the elongated structure is an artery, determine the elongated structure is aneurysmal.

3. The system according to claim 1 , wherein the electronic processor is further configured to

determine a diameter of the elongated structure in the two dimensional cross section by using the line fitted to the elongated structure in the two dimensional cross section converted to polar coordinates.

4. The system according to claim 1 , wherein the line fitted to the elongated structure in the two dimensional cross section indicates a circumference of the elongated structure.

5. The system according to claim 1 , wherein the elongated structure is one selected from the group consisting of an artery, a colon, or an esophagus.

6. The system according to claim 1 , wherein the electronic processor is configured to determine the centerline of the elongated structure in the three dimensional medical image by using artificial intelligence software.

7. The system according to claim 6 , wherein the electronic processor is further configured to train the artificial intelligence software to determine the centerline of the elongated structure in the three dimensional medical image using a training set including a plurality of training examples.

8. The system according to claim 7 , wherein the electronic processor is further configured to create a training example by

receiving a plurality of reference points for a subset of a plurality of slices of a second three dimensional medical image, each of the plurality of reference points marking a centerline of the elongated structure within one of the subset of the plurality of slices of the second three dimensional medical image;

receiving a selection of a starting reference point within the plurality of reference points;

determining an order of the plurality of reference points; and

fitting a spline curve to the plurality of reference points based on the order of the reference points to define a marked centerline of the second three medical dimensional image to create the training example.

9. A method of determining an abnormality in an elongated structure in a three dimensional medical image, the method comprising

determining a centerline of the elongated structure in the three dimensional medical image;

determining a plurality of two dimensional cross sections of the three dimensional medical image based on the centerline; and

for each two dimensional cross section of the plurality of two dimensional cross sections;

converting the two dimensional cross section to polar coordinates;

fitting a line to the elongated structure in the two dimensional cross section converted to polar coordinates; and

reconverting the two dimensional cross section to Cartesian coordinates.

10. The method according to claim 9 , the method further comprising:

determining a diameter of the elongated structure in the two dimensional cross section; and

when the diameter exceeds a predetermined threshold and the elongated structure is an artery, determining the elongated structure is aneurysmal.

11. The method according to claim 9 , the method further comprising

determining a diameter of the elongated structure in the two dimensional cross section by using the line fitted to the elongated structure in the two dimensional cross section converted to polar coordinates.

12. The method according to claim 9 , wherein the line fitted to the elongated structure in the two dimensional cross section indicates a circumference of the elongated structure.

13. The method according to claim 9 , wherein the elongated structure is one selected from a group consisting of an artery, a colon, or an esophagus.

14. The method according to claim 9 , wherein determining the centerline of the elongated structure in the three dimensional medical image includes using artificial intelligence software to determine the centerline.

15. The method according to claim 14 , wherein the method further comprising: training the artificial intelligence software to determine the centerline of the elongated structure in the three dimensional medical image using a training set including a plurality of training examples.

16. The system according to claim 15 , the method further comprising:

creating a training example for the plurality of training examples by:

receiving a plurality of reference points for a subset of a plurality of slices of a second three dimensional medical image, each of the plurality of reference points marking a centerline of the elongated structure within one of the subset of the plurality of slices of the second three dimensional medical image;

receiving a selection of a starting reference point within the plurality of reference points;

determining an order of the plurality of reference points; and

fitting a spline curve to the plurality of reference points based on the order of the reference points to define a marked centerline of the second three dimensional medical image to create the training example.