Abstract: A technique for automating the identifying of a measurement point in cephalometric image analysis is provided. An automatic measurement point recognition method includes a step of detecting, from a cephalometric image 14 acquired from a subject, a plurality of peripheral partial regions 31, 32, 33, 34 for recognizing a target feature point, a step of estimating a candidate position of the feature point in each of the peripheral partial regions 31, 32, 33, 34 by the application of a regression CNN model 10, and a step of determining the position of the feature point in the cephalometric image 14 based on the distribution of the candidate positions estimated. In the step of detecting, for example, the peripheral partial region 32, a classification CNN model 13 trained with a control image 52 is applied.

Abstract: A technique for automating the identifying of a measurement point in cephalometric image analysis is provided. An automatic measurement point recognition method includes a step of detecting, from a cephalometric image 14 acquired from a subject, a plurality of peripheral partial regions 31, 32, 33, 34 for recognizing a target feature point, a step of estimating a candidate position of the feature point in each of the peripheral partial regions 31, 32, 33, 34 by the application of a regression CNN model 10, and a step of determining the position of the feature point in the cephalometric image 14 based on the distribution of the candidate positions estimated. In the step of detecting, for example, the peripheral partial region 32, a classification CNN model 13 trained with a control image 52 is applied.

Abstract: To have convenient and highly precise prediction of the shape of a human body after a treatment by calculation processing that includes extracting a feature vector Fnew from face data of a patient as an evaluation subject, selecting a plurality of case patients having feature vectors Fpre(i), extracted from the face data of a plurality of previous patients, obtaining pre-orthodontic facial shape models Hpre(i) and a post-orthodontic facial shape models Hpost(i) in which the faces of the selected previous case patients before and after a treatment have been normalized, obtaining a facial shape model Hnew, and obtaining a three-dimensional predicted facial shape model Hprd, by modifying the facial shape model Hnew of the patient as an evaluation subject, using a vector average difference AVEpost?AVEpre between the pre-treatment and post-treatment facial shape models.

Abstract: A technique for automating the identifying of a measurement point in cephalometric image analysis is provided. An automatic measurement point recognition method includes a step of detecting, from a cephalometric image 14 acquired from a subject, a plurality of peripheral partial regions 31, 32, 33, 34 for recognizing a target feature point, a step of estimating a candidate position of the feature point in each of the peripheral partial regions 31, 32, 33, 34 by the application of a regression CNN model 10, and a step of determining the position of the feature point in the cephalometric image 14 based on the distribution of the candidate positions estimated. In the step of detecting, for example, the peripheral partial region 32, a classification CNN model 13 trained with a control image 52 is applied.

Abstract: To have convenient and highly precise prediction of the shape of a human body after a treatment by calculation processing that includes extracting a feature vector Fnew from face data of a patient as an evaluation subject, selecting a plurality of case patients having feature vectors Fpre(i), extracted from the face data of a plurality of previous patients, obtaining pre-orthodontic facial shape models Hpre(i) and a post-orthodontic facial shape models Hpost(i) in which the faces of the selected previous case patients before and after a treatment have been normalized, obtaining a facial shape model Hnew, and obtaining a three-dimensional predicted facial shape model Hprd, by modifying the facial shape model Hnew of the patient as an evaluation subject, using a vector average difference AVEpost?AVEpre between the pre-treatment and post-treatment facial shape models.

Abstract: To have convenient and highly precise prediction of the shape of a human body after a treatment by calculation processing that includes extracting a feature vector Fnew from face data of a patient as an evaluation subject, selecting a plurality of case patients having feature vectors Fpre(i), extracted from the face data of a plurality of previous patients, obtaining pre-orthodontic facial shape models Hpre(i) and a post-orthodontic facial shape models Hpost(i) in which the faces of the selected previous case patients before and after a treatment have been normalized, obtaining a facial shape model Hnew, and obtaining a three-dimensional predicted facial shape model Hprd, by modifying the facial shape model Hnew of the patient as an evaluation subject, using a vector average difference AVEpost?AVEpre between the pre-treatment and post-treatment facial shape models.

Abstract: To have convenient and highly precise prediction of the shape of a human body after a treatment by calculation processing.