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 computer-implemented method of virtually representing an orthodontic treatment outcome of a patient's teeth, the computer-implemented method comprising:
receiving a 2D facial image of a patient;
identifying facial landmarks on the 2D facial image;
generating a facial midline based on the landmarks;
forming a facial midline plane based on the facial midline;
receiving a 3D tooth model having a dental midline; and
inserting the 3D tooth model into the 2D facial image of the patient by (i) aligning the dental midline of the 3D tooth model with the facial midline plane of the 2D facial image and (ii) projecting a contour of the 3D tooth model in an image plane of the 2D facial image.
2. The computer-implemented method of claim 1 , wherein:
the facial landmarks are symmetric landmarks.
3. The computer-implemented method of claim 2 , wherein each of the symmetric landmarks is a midpoint between a corresponding pair of facial landmarks, a first of the pair identifying a feature on a left side of the patient's face and a second of the pair identifying the same feature on the right side of the face.
4. The computer-implemented method of claim 1 , wherein:
the facial landmarks are central landmarks.
5. The computer-implemented method of claim 4 , wherein each of the central landmarks is one of a nasal ridge landmark, a nose tip landmark, a center lip landmark, and a center chin landmark.
6. The computer-implemented method of claim 1 , wherein:
the facial landmarks are symmetric landmarks and central landmarks.
7. The computer-implemented method of claim 1 , wherein generating the facial midline based on the landmarks comprises:
generating a plurality of interim facial midlines and determining the R-squared fit between each of the facial midlines and the facial landmarks; and
wherein the facial midline is the interim facial midline with the highest R-squared fit.
8. The computer-implemented method of claim 1 , wherein generating the facial midline based on the landmarks comprises:
generating a plurality of interim facial midlines and determining a sum of the square of the distances of the facial midlines and the facial landmarks; and
wherein the facial midline is the interim facial midline with the lowest sum of the square of the distances.
9. The computer-implemented method of claim 8 , wherein one or more of the facial landmarks is assigned a weight used in determining a sum of the square of the distances of the facial midlines and the facial landmarks.
10. The computer-implemented method of claim 1 , wherein aligning the 3D tooth model with the 2D facial image of the patient by aligning the dental midline with the facial midline plane comprise one or more of rotation of the 2D facial image, rotating the 3D tooth model, and translating the 3D tooth model.
11. The computer-implemented method of claim 1 , further comprising rendering, on a display, a composite image of a 2D rendering of the 3D tooth model and the 2D facial image.
12. The computer-implemented method of claim 1 , wherein the facial landmarks on the 2D facial image is identified at least in part using a machine learning algorithm.
13. The computer-implemented method of claim 1 , wherein the contour of 3D tooth model comprises a lip contour or a tooth contour.