WO2021213176A1 - 一种基于大数据的口内修复体数字化设计方法 - Google Patents
一种基于大数据的口内修复体数字化设计方法 Download PDFInfo
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- the invention belongs to the field of oral restoration and restoration technology, and specifically relates to a digital design method for an intraoral restoration based on big data.
- the existing digital design of intraoral prosthesis can solve the requirements of digital design; however, due to too many manual interactive design operations in the design software, firstly, the design efficiency is low; secondly, too many manual interactive design operations lead to design accuracy
- the design effect is not high, and the design effect is overly dependent on the operator's experience and operating skills, and sometimes it needs to be redesigned to complete the design task; third, the expert experience takes a long time to accumulate, and the junior technicians need to memorize a large number of cases to design qualified products.
- the present invention provides a digital design method for intraoral prosthesis based on big data, which greatly reduces the excessive manual interactive design operations in the existing digital design software for intraoral prosthesis.
- the present invention achieves the above-mentioned technical objects through the following technical means.
- a digital design method for intraoral prosthesis based on big data including the following steps:
- the feature annotation includes the feature point annotation of the missing tooth gap and the feature point annotation of the remaining tooth.
- the feature points of the missing tooth gap include feature points at both ends of the gap and gap control points.
- the characteristic points of the remaining teeth include the characteristic points of the central area of the top of the remaining teeth, the vertex of the lingual and palatal gingival margin of the remaining teeth, and the apex of the lingual and palatal gingival papilla of the remaining teeth.
- transformation function T21 is specifically:
- T21_error (P1-T21_origin*P2 )/P2;
- design solution D1 is specifically:
- the restoration technician makes adjustments according to the design plan D1 to make it suitable for the individual needs of the current dental model.
- S7 which uses the data M1, P1 and D1 as the new sample S1 and collects it in the standard template database L, where D1 is the characteristic data of the intraoral prosthesis of the sample S1.
- the present invention obtains the dental model feature data P2 closest to the current dental jaw feature data P1 by customizing the distance function of the dental jaw feature data, that is, finds the most matching standard template data S2 (including P2, P2 where P2 and P2 are located in the standard template database L).
- the dental model entity data M2, P2 of the sample contains the intraoral restoration feature data D2), and then M2, P2, the current dental model data M1, and the current dental model data P1 are used as the input of the non-rigid point matching algorithm to obtain Transformation function T21; Finally, the transformation function T21 is applied to D2, and the design plan D1 is obtained by the adaptive surface fitting algorithm for reference by the restoration technician.
- the technical scheme of the present invention can be directly used in the field of digital design of intraoral prostheses, greatly reducing the excessive manual interactive design operations in the existing digital design software such as removable partial dentures, and quickly improving the design level, design efficiency and design efficiency of operators. Design accuracy.
- Figure 1 is a schematic diagram of the dental jaw model of the present invention
- Figure 2 is a schematic diagram of the dental jaw model for installing the intraoral prosthesis according to the present invention
- Fig. 3 is a schematic diagram of the current dental model according to the present invention.
- FIG. 4 is a schematic diagram of the current dental model with the intraoral prosthesis installed according to the present invention.
- 1-dental model entity data 2-dental model feature data
- 3-intraoral restoration feature data 4-current dental model data
- 5-current dental jaw feature data 6-current intraoral restoration feature data .
- a digital design method for intraoral prosthesis based on big data which specifically includes the following steps:
- Step (1) three-dimensional scanning to obtain dental model entity data M (corresponding to 1 in Figure 1), and feature annotation of the dental model entity data M to obtain dental model feature data P (corresponding to 2 in Figure 1); Scan to obtain the characteristic data D of the intraoral prosthesis (corresponding to 3 in Figure 2), the three as a whole as a sample S, the multiple samples obtained by scanning constitute a removable partial denture (RPD) standard template database L, standard template
- RPD removable partial denture
- the feature labeling needs to cover the relevant positions of the intraoral restoration. Taking the application of removable partial dentures as an example, a practical method for labeling feature points is as follows:
- 2Gap control points Preferably, additional intermediate control points can be added to the buccal area in the middle of the gap.
- Step (2) three-dimensional scanning of the current dental model that needs intraoral restoration, the current dental model data is marked as M1 (corresponding to 4 in Figure 3), and feature annotation is performed to obtain the current dental jaw characteristic data, marked as P1( Corresponding to 5) in Figure 3.
- This feature labeling needs to cover the relevant positions of the intraoral restoration. Taking the application of removable partial dentures as an example, a practical method for labeling feature points is as follows:
- 2Gap control points Preferably, additional intermediate control points can be added to the buccal area in the middle of the gap.
- Step (3) Customize the distance function f(Px,Py) of the dental jaw characteristic data, calculate the distance between the current dental jaw characteristic data P1 and the dental jaw model characteristic data Pn (n ⁇ 2) of the standard template database L to obtain
- the characteristic data of the dental model closest to the current dental characteristic data P1 is set to P2; that is, the most matching standard template data S2 is found in the standard template database L, including M2, P2, and D2, where M2 is the tooth of the sample where P2 is located.
- the jaw model entity data, D2 is the characteristic data of the intraoral restoration of the sample where P2 is located.
- the distance function f(Px, Py) of the dental jaw characteristic data taking removable partial dentures as an example, a practically applicable distance function f(Px, Py) is defined as follows:
- Step (3.1) find the coordinate mean values of all the feature points of the current dental jaw feature data P1 and dental model feature data P2 as P_mean1, P_mean2, and subtract the corresponding mean value P_mean1 from each feature point sequence in P1 and P2 P_mean2, complete translation transformation;
- Step (3.2) the feature point sequence after translation transformation in P1 is multiplied by a coefficient ⁇ 1, and the feature point sequence after translation transformation in P2 is multiplied by a coefficient ⁇ 2 to complete the normalization of the shape and size;
- Step (3.3) using the three-dimensional rotation transformation matrix, align the shape of the feature point sequence of P1 and P2 together, so that the Platts distance between the two shapes is the smallest, and the Platts distance is the distance function f(Px,Py ).
- Step (4) through the non-rigid point matching algorithm, M2, P2, M1, and P1 are used as the algorithm input to obtain the transformation function T21.
- Step (4.1) input P1 and P2 to the non-rigid point matching algorithm
- Step (4.2) using PCA (Principal Component Analysis) algorithm to initially register P1 and P2;
- step (5) the transformation function T21 is applied to D2 (ie, T21*D2), and the design scheme D1 is obtained by using an adaptive surface fitting algorithm.
- Step (5.1) apply the transformation function T21 to D2 to obtain the preliminary result D1_origin of the feature point;
- Step (5.2) through traversal calculation, obtain the closest point on the current dental model data M1 to each point of the feature point preliminary result D1_origin, and move each point on the feature point preliminary result D1_orgin to the closest point, namely Obtain the design plan D1.
- step (6) the restoration technician makes adjustments according to the design plan D1 to make it suitable for the individual needs of the current dental model.
- Step (7) the data M1, P1 and D1 are collected as a new sample S1 in the standard template database L, where D1 is the characteristic data of the intraoral restoration of the sample S1 (corresponding to 6 in Fig. 4).
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Abstract
一种基于大数据的口内修复体数字化设计方法，属于口腔修复及修复工艺领域，自定义牙颌特征数据距离函数，获得与当前牙颌特征数据P1距离最近的牙颌模型特征数据P2，再将P2、P2所在样本的牙颌模型实体数据M2、当前牙颌模型数据M1、当前牙颌特征数据P1作为非刚性点匹配算法的输入，获得变换函数T21；最后将变换函数T21作用在口内修复体特征数据D2上，利用自适应表面贴合算法获得设计方案D1，供修复体技工参考。所述方法大幅度减少现有技术中过多的手工交互设计操作，快速提高操作人员的设计水平、设计效率及设计精度。
Description
本发明属于口腔修复及修复工艺领域，具体涉及一种基于大数据的口内修复体数字化设计方法。
现有口内修复体数字化设计，虽然能够解决数字化设计的要求；但由于在设计软件中过多的手工交互设计操作，一是导致设计效率较低；二是过多的手工交互设计操作导致设计精度不高、设计效果过渡依赖操作者的经验和操作技巧，有时需要重新设计才能完成设计任务；三是专家经验需要较长时间积累，初级技工需要记忆大量案例才能设计合格的产品。
发明内容
针对现有技术中存在不足，本发明提供了一种基于大数据的口内修复体数字化设计方法，大幅度减少现有口内修复体数字化设计软件中过多的手工交互设计操作。
本发明是通过以下技术手段实现上述技术目的的。
一种基于大数据的口内修复体数字化设计方法，包括以下步骤：
S1，三维扫描牙颌模型、口内修复体，分别获得牙颌模型实体数据M、口内修复体特征数据D，对所述M特征标注获得牙颌模型特征数据P，所述M、D和P作为一个样本，多个样本构成可摘除局部义齿标准模板数据库L；
S2，三维扫描需要进行口内修复的当前牙颌模型，获得前牙颌模型数据记为M1，对所述M1特征标注获得当前牙颌特征数据P1；
S3，自定义牙颌特征数据距离函数f(Px,Py)，由此计算所述P1与所述L中牙颌模型特征数据Pn之间的距离，获得与P1距离最近的牙颌模型特征数据P2，P2所在样本的牙颌模型实体数据为M2，P2所在样本的口内修复体特征数据为D2，其中n≥2；
S4，利用非刚性点匹配算法，将所述M2、P2、M1、P1作为算法输入，获得变换函数T21；
S5，将变换函数T21作用在D2上，利用自适应表面贴合算法获得设计方案D1。
进一步，所述特征标注包括缺牙间隙特征点标注和余留牙特征点标注。
更进一步，所述缺牙间隙特征点包括间隙两端特征点和间隙控制点。
更进一步，所述余留牙特征点包括余留牙顶部中央区域特征点、余留牙牙齿舌腭侧龈缘顶点以及余留牙舌腭侧牙龈乳头顶点。
进一步，所述距离函数f(Px,Py)，具体为：
1)将所述P1、P2中每个特征点序列减去对应的均值P_mean1、P_mean2，完成平移变换；
2)所述平移变换的特征点序列乘以一个系数，完成形状大小的归一化；
3)利用三维旋转变换矩阵，将P1和P2的特征点序列形状对齐在一起，使得两个形 状的普氏距离最小，该普氏距离即为所述距离函数f(Px,Py)。
进一步，所述变换函数T21，具体为：
1)非刚性点匹配算法输入P1和P2；
2)利用主成分分析法初步配准P1和P2；
3)采用普氏分析求解配准后的P1和P2三维变换函数T21_origin，通过配准后的P1和P2对应特征点的匹配误差，计算获得线性插值修正函数T21_error，T21_error＝(P1-T21_origin*P2)/P2；所述变换函数T21表示为：T21＝T21_origin+T21_error。
更进一步，所述设计方案D1，具体为：
1)将变换函数T21作用在D2上，获得特征点初步结果D1_origin；
2)通过遍历计算，获取当前牙颌模型数据M1上与特征点初步结果D1_origin各点距离最近的点，并将特征点初步结果D1_orgin上的各点分别移动到距离最近的点，即获得设计方案D1。
更进一步，还包括S6，修复体技工根据设计方案D1做出调整，使其适合当前牙颌模型的个性化需求。
更进一步，还包括S7，将数据所述M1、P1和D1作为新样本S1，收集在标准模板数据库L，其中D1是样本S1的口内修复体特征数据。
本发明的有益效果为：
本发明通过自定义牙颌特征数据距离函数，获得与当前牙颌特征数据P1距离最近的牙颌模型特征数据P2，即在标准模板数据库L找到最为匹配的标准模板数据S2(包括P2、P2所在样本的牙颌模型实体数据M2、P2所在样本的口内修复体特征数据D2)，再将M2、P2、当前牙颌模型数据M1、当前牙颌特征数据P1作为非刚性点匹配算法的输入，获得变换函数T21；最后将变换函数T21作用在D2上，利用自适应表面贴合算法获得设计方案D1，供修复体技工参考。本发明的技术方案能直接用于口内修复体的数字设计领域，大幅度减少现有可摘局部义齿等数字化设计软件中过多的手工交互设计操作，快速提高操作人员的设计水平、设计效率及设计精度。
图1为本发明所述牙颌模型示意图；
图2为本发明所述安装口内修复体的牙颌模型示意图；
图3为本发明所述当前牙颌模型示意图；
图4为本发明所述安装口内修复体的当前牙颌模型示意图；
其中，1-牙颌模型实体数据，2-牙颌模型特征数据，3-口内修复体特征数据，4-当前牙颌模型数据，5-当前牙颌特征数据，6-当前口内修复体特征数据。
下面结合附图以及具体实施例对本发明作进一步的说明，但本发明的保护范围并不限于此。
一种基于大数据的口内修复体数字化设计方法，具体包括以下步骤：
步骤(1)，三维扫描获得牙颌模型实体数据M(对应图1中的1)，并对牙颌模型实体 数据M进行特征标注获得牙颌模型特征数据P(对应图1中的2)；扫描获得口内修复体特征数据D(对应图2中的3)，三者整体作为一个样本S，扫描得到的多个样本组成可摘除局部义齿(removable partial denture，RPD)标准模板数据库L，标准模板数据库L中的样本个数覆盖各种常见的牙列缺失类型。
所述特征标注需要涵盖口内修复体相关的位置，以可摘除局部义齿应用为例，一种实际可以应用的特征点标注方法如下：
(1)标注缺牙间隙特征点(图1的圆点)
①间隙两端特征点：除后牙磨垫位置外，间隙两端的两个特征点需尽量靠近牙齿根部；
②间隙控制点(可选)：优选的，可以在间隙中部靠颊侧区域添加额外的若干中间控制点。
(2)标注余留牙的三类特征点
①余留牙顶部中央区域特征点(图1的三角点)：尖牙(牙尖顶点)、切牙(切缘中点)和后牙(
面中点)；
②余留牙牙齿舌腭侧龈缘顶点(Zenith点)(图1的正方形点)；
③余留牙舌腭侧牙龈乳头顶点(图1的五角星点)。
步骤(2)，三维扫描需要进行口内修复的当前牙颌模型，当前牙颌模型数据记为M1(对应图3中的4)，并进行特征标注，获得当前牙颌特征数据，记为P1(对应图3中的5)。
该特征标注需要涵盖口内修复体相关的位置，以可摘除局部义齿应用为例，一种实际可以应用的特征点标注方法如下：
(1)标注缺牙间隙特征点(图3的圆点)
①间隙两端特征点：除后牙磨垫位置外，间隙两端的两个特征点需尽量靠近牙齿根部；
②间隙控制点(可选)：优选的，可以在间隙中部靠颊侧区域添加额外的若干中间控制点。
(2)标注余留牙的三类特征点
①余留牙顶部中央区域特征点标注(图3的三角点)：尖牙(牙尖顶点)、切牙(切缘中点)和后牙(
面中点)；
②余留牙牙齿舌腭侧龈缘顶点(Zenith点)(图3的正方形点)；
③余留牙舌腭侧牙龈乳头顶点(图3的五角星点)。
步骤(3)，自定义牙颌特征数据距离函数f(Px,Py)，计算当前牙颌特征数据P1与标准模板数据库L的牙颌模型特征数据Pn(n≥2)之间的距离，获得与当前牙颌特征数据P1距离最近的牙颌模型特征数据，设为P2；即在标准模板数据库L找到最为匹配的标准模板数据S2，包括M2、P2和D2，其中M2是P2所在样本的牙颌模型实体数据，D2是P2所在样本的口内修复体特征数据。
该所述牙颌特征数据距离函数f(Px,Py)，以可摘除局部义齿为例，一种实际可以应用的距离函数f(Px,Py)定义如下：
步骤(3.1)，求当前牙颌特征数据P1和牙颌模型特征数据P2的所有特征点的坐标均值分别为P_mean1、P_mean2，将P1和P2中的每个特征点序列减去其对应均值P_mean1、P_ mean2，完成平移变换；
步骤(3.2)，P1中平移变换后的特征点序列乘以一个系数α1，P2中平移变换后的特征点序列乘以一个系数α2，完成形状大小的归一化；
步骤(3.3)，利用三维旋转变换矩阵，将P1和P2的特征点序列形状对齐在一起，使得两个形状的普氏距离最小，该普氏距离即为需要计算的距离函数f(Px,Py)。
步骤(4)，通过非刚性点匹配算法，把M2、P2、M1、P1作为算法输入，获得变换函数T21。
该算法只需要满足非刚性匹配即可，一种实际中可以应用的匹配算法步骤如下：
步骤(4.1)，非刚性点匹配算法输入P1和P2；
步骤(4.2)，利用PCA(Principal Component Analysis，主成分分析)算法初步配准P1和P2；
步骤(4.3)，采用Procrustes Analysis(普氏分析)求解配准后的P1和P2三维变换函数T21_origin(T21_origin为三维变换矩阵)，并通过配准后的P1和P2对应特征点的匹配误差，计算获得线性插值修正函数T21_error，T21_error＝(P1-T21_origin*P2)/P2；
步骤(4.4)，变换函数T21表示为：T21＝T21_origin+T21_error。
步骤(5)，变换函数T21作用在D2上(即T21*D2)，并利用自适应表面贴合算法获得设计方案D1。
一种实际中可以应用的自适应贴合算法步骤如下：
步骤(5.1)，将变换函数T21作用在D2上，获得特征点初步结果D1_origin；
步骤(5.2)，通过遍历计算，获取当前牙颌模型数据M1上与特征点初步结果D1_origin各点距离最近的点，并将特征点初步结果D1_orgin上的各点分别移动到距离最近的点，即获得设计方案D1。
步骤(6)，修复体技工根据设计方案D1做出调整，使其适合当前牙颌模型的个性化需求。
步骤(7)，将数据M1、P1和D1作为一个新的样本S1，收集在标准模板数据库L，其中D1是样本S1的口内修复体特征数据(对应图4中的6)。
所述实施例为本发明的优选的实施方式，但本发明并不限于上述实施方式，在不背离本发明的实质内容的情况下，本领域技术人员能够做出的任何显而易见的改进、替换或变型均属于本发明的保护范围。
Claims (8)
- 一种基于大数据的口内修复体数字化设计方法，其特征在于，包括以下步骤：S1，三维扫描牙颌模型、口内修复体，分别获得牙颌模型实体数据M、口内修复体特征数据D，对所述M特征标注获得牙颌模型特征数据P，所述M、D和P作为一个样本，多个样本构成可摘除局部义齿标准模板数据库L；S2，三维扫描需要进行口内修复的当前牙颌模型，获得前牙颌模型数据记为M1，对所述M1特征标注获得当前牙颌特征数据P1；S3，自定义牙颌特征数据距离函数f(Px,Py)，由此计算所述P1与所述L中牙颌模型特征数据Pn之间的距离，获得与P1距离最近的牙颌模型特征数据P2，P2所在样本的牙颌模型实体数据为M2，P2所在样本的口内修复体特征数据为D2，其中n≥2；S4，利用非刚性点匹配算法，将所述M2、P2、M1、P1作为算法输入，获得变换函数T21；所述变换函数T21，具体为：1)非刚性点匹配算法输入P1和P2；2)利用主成分分析法初步配准P1和P2；3)采用普氏分析求解配准后的P1和P2三维变换函数T21_origin，通过配准后的P1和P2对应特征点的匹配误差，计算获得线性插值修正函数T21_error，T21_error＝(P1-T21_origin*P2)/P2；所述变换函数T21表示为：T21＝T21_origin+T21_error；S5，将变换函数T21作用在D2上，利用自适应表面贴合算法获得设计方案D1。
- 根据权利要求1所述的基于大数据的口内修复体数字化设计方法，其特征在于，所述特征标注包括缺牙间隙特征点标注和余留牙特征点标注。
- 根据权利要求2所述的基于大数据的口内修复体数字化设计方法，其特征在于，所述缺牙间隙特征点包括间隙两端特征点和间隙控制点。
- 根据权利要求2所述的基于大数据的口内修复体数字化设计方法，其特征在于，所述余留牙特征点包括余留牙顶部中央区域特征点、余留牙牙齿舌腭侧龈缘顶点以及余留牙舌腭侧牙龈乳头顶点。
- 根据权利要求1所述的基于大数据的口内修复体数字化设计方法，其特征在于，所述距离函数f(Px,Py)，具体为：1)将所述P1、Pn中每个特征点序列减去对应的均值P_mean1、P_meann，完成平移变换；2)所述平移变换的特征点序列乘以一个系数，完成形状大小的归一化；3)利用三维旋转变换矩阵，将P1和Pn的特征点序列形状对齐在一起，使得两个形状的普氏距离最小，该普氏距离即为所述距离函数f(Px,Py)。
- 根据权利要求1所述的基于大数据的口内修复体数字化设计方法，其特征在于，所述设计方案D1，具体为：1)将变换函数T21作用在D2上，获得特征点初步结果D1_origin；2)通过遍历计算，获取当前牙颌模型数据M1上与特征点初步结果D1_origin各点距离最近的点，并将特征点初步结果D1_orgin上的各点分别移动到距离最近的点，即获得设计方案D1。
- 根据权利要求6所述的基于大数据的口内修复体数字化设计方法，其特征在于，还包括S6，修复体技工根据设计方案D1做出调整，使其适合当前牙颌模型的个性化需求。
- 根据权利要求7所述的基于大数据的口内修复体数字化设计方法，其特征在于，还包 括S7，将数据所述M1、P1和D1作为新样本S1，收集在标准模板数据库L，其中D1是样本S1的口内修复体特征数据。
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