Automated 2D/3D integration and lip spline autoplacement
A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.
1. A method for virtually representing an orthodontic treatment outcome of a patient's teeth, the computer-implemented method comprising:
receiving a facial image of a patient;
identifying a facial landmark on the facial image, wherein the facial landmark comprises at least a lip landmark;
locating a mouth opening within the facial image based at least in part on the lip landmark;
extracting a first set of teeth contours within the mouth opening;
extracting a second set of teeth contours from a 3D model representing the patient's teeth rendered in an intermediate position between an initial position and a final position, wherein the intermediate position and the final position are part of a treatment plan;
aligning the extracted second set of teeth contours with the first set of teeth contours within the mouth opening; and
inserting the 3D model into the facial image to form a composite image.
2. The method of claim 1 , further comprising:
inserting a rendering of the 3D model into the mouth opening based at least in part on the alignment of the second set of teeth contours from the 3D model with the first set of teeth contours of the patient's teeth within the mouth opening in the facial image.
3. The method of claim 1 , wherein locating the mouth opening within the facial image comprises cropping the facial image around the lip landmark to exclude other facial landmarks.
4. The method of claim 3 , wherein the other facial landmarks include one or more of eye, nose, or facial outline landmarks.
5. The method of claim 1 , wherein extracting the first set of teeth contours within the mouth opening comprises detecting the first set of tooth contours using a convolutional neural network.
6. The method of claim 5 , wherein the convolutional neural network comprises a holistic edge detection deep learning model.
7. The method of claim 1 , wherein the first set of teeth contours comprises a plurality of pixels, each pixel of the first set of teeth contours having a value to be compared with a pre-determined threshold value.
8. The method of claim 1 , wherein the facial image of the patient is a 2D image.
9. The method of claim 1 , further comprising:
rendering the composite image on a display, wherein the composite image comprises a 2D rendering of the 3D model.
10. The method of claim 1 , further comprising:
generating a set of thinned teeth contours from the first set of teeth contours.
11. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:
receiving a facial image of a patient;
identifying a facial landmark on the facial image, wherein the facial landmark comprises at least a lip landmark;
locating a mouth opening within the facial image based at least in part on the lip landmark;
extracting a first set of teeth contours within the mouth opening;
extracting a second set of teeth contours from a 3D model representing the patient's teeth rendered in an intermediate position between an initial position and a final position, wherein the intermediate position and the final position are part of a treatment plan; and
aligning the extracted second set of teeth contours with the first set of teeth contours within the mouth opening; and
inserting the 3D model into the facial image to form a composite image.
12. The non-transitory computer-readable storage medium of claim 11 , wherein the operations further comprise:
inserting a rendering of the 3D model into the mouth opening based at least in part on the alignment of the second set of teeth contours from the 3D model with the first set of teeth contours of the patient's teeth within the mouth opening in the facial image.
13. The non-transitory computer-readable storage medium of claim 11 , wherein locating the mouth opening within the facial image comprises cropping the facial image around the lip landmark to exclude other facial landmarks.
14. The non-transitory computer-readable storage medium of claim 13 , wherein the other facial landmarks include one or more of eye, nose, or facial outline landmarks.
15. The non-transitory computer-readable storage medium of claim 11 , wherein extracting the first set of teeth contours within the mouth opening comprises detecting the first set of tooth contours using a convolutional neural network.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the convolutional neural network comprises a holistic edge detection deep learning model.
17. The non-transitory computer-readable storage medium of claim 11 , wherein the first set of teeth contours comprises a plurality of pixels, each pixel of the first set of teeth contours having a value to be compared with a pre-determined threshold value.
18. The non-transitory computer-readable storage medium of claim 11 , wherein the facial image of the patient is a 2D image.
19. The non-transitory computer-readable storage medium of claim 11 , wherein the operations further comprise rendering the composite image on a display, wherein the composite image comprises a 2D rendering of the 3D model.
20. The non-transitory computer-readable storage medium of claim 11 , wherein the operations further comprise:
generating a set of thinned teeth contours from the first set of teeth contours.