CN116309909A - Oral cavity scanning image processing method and device - Google Patents
Oral cavity scanning image processing method and device Download PDFInfo
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Abstract
The application discloses an oral scanning image processing method and device, wherein the method comprises the following steps: determining a alveolar process associated region based on a sagittal projection image corresponding to the oral scan image, the alveolar process associated region including a mandibular alveolar process region and at least a portion of a mandibular ascending branch region; obtaining an axial projection image corresponding to a alveolar process association area in an oral scanning image; image segmentation is carried out on the axial projection image to obtain an arch mask image; a dental arch curve is obtained based on the dental arch mask image. The relevant area of the tooth socket process is not influenced by the visual field condition and the tooth condition in the oral cavity, so that the process of obtaining the dental arch curve is not limited by the visual field condition and the tooth condition in the oral cavity, and in this way, stable and accurate dental arch mask images can be obtained under the conditions of CBCT images with small and small local visual fields and complex tooth conditions (complete mouth loss, mixed dentition, metal artifacts and the like), and stable and accurate dental arch curves can be obtained based on the dental arch mask images.
Description
Technical Field
The present application relates to the field of dental image processing technology, and in particular, to an oral scanning image processing method, an oral scanning image processing apparatus, an electronic device, and a computer readable storage medium.
Background
Dental arches are ideal curves tangent to the dentition, and fitting and generating dental arches are of great importance in aided correction design and simulated tooth alignment experiments. With the development of oral treatment and orthodontics, the requirements for dental arch curve fitting are also increasing, and various geometric curves (parabolas, ellipsoids, catenaries, etc.) and mathematical functions (beta functions, the Fourier Series, multi-curved cosine functions, etc.) are applied to the description of dental arch morphology.
Currently, teeth are adopted as a basis for obtaining an arch mask image, and the requirements on the integrity of a visual field are high (for example, the requirements on the display of jawbone in a CBCT image are basically complete, the tooth condition is good, no obvious missing exists, and the like), and for a local small-and-medium-visual-field CBCT image and a scene with complicated tooth condition (full-mouth missing, mixed dentition, metal artifact, and the like), a stable and accurate arch curve cannot be generated.
Disclosure of Invention
The application provides an oral scanning image processing method, an oral scanning image processing device, electronic equipment and a computer readable storage medium, so as to solve the problem that a stable and accurate dental arch curve cannot be generated for a CBCT image with a local small and medium visual field and a scene with complex tooth condition in the prior art.
To solve or improve the above technical problem to some extent, according to an aspect of the present application, there is provided a dental arch curve generating method, including:
determining a alveolar process associated region based on a sagittal projection image corresponding to the oral scan image, the alveolar process associated region including a mandibular alveolar process region and at least a portion of a mandibular ascending branch region;
obtaining an axial projection image corresponding to the alveolar process association area in the oral scanning image;
image segmentation is carried out on the axial projection image to obtain an arch mask image;
and obtaining a dental arch curve based on the dental arch mask image.
In some embodiments, the obtaining an arch curve based on the arch mask image comprises:
performing distance transformation on the dental arch mask image to obtain a distance transformation image,
dividing the distance transformation image by using a skeleton extraction model to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image;
performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image;
and obtaining an arch curve based on the target arch skeleton image.
In some embodiments, the obtaining an arch curve based on the target arch skeleton image comprises:
and processing the target dental arch skeleton image by using a cubic spline interpolation method to obtain the dental arch curve.
In some embodiments, the determining the alveolar process correlation area based on the sagittal projection image corresponding to the oral scan image includes:
inputting the sagittal projection image corresponding to the oral cavity scanning image into a alveolar process association area extraction model to obtain a mask image of the alveolar process association area output by the alveolar process association area extraction model, wherein the alveolar process association area extraction model is a pre-trained deep neural network model for outputting the mask image of the corresponding alveolar process association area based on the input sagittal projection image.
In some embodiments, the obtaining an axial projection image corresponding to the alveolar process association area in the oral scan image includes:
and carrying out maximum density projection on the area in the range of the alveolar process association area in the oral cavity scanning image according to the axial direction to obtain the axial projection image.
In some embodiments, the image segmentation of the axial projection image to obtain an arch mask image includes:
inputting the axial projection image into a dental arch mask segmentation model to obtain the dental arch mask image output by the dental arch mask segmentation model, wherein the dental arch mask segmentation model is a pre-trained deep neural network model for outputting a corresponding dental arch mask image based on the input axial projection image.
In some embodiments, the method further comprises:
obtaining a dental arch thickness based on the dental arch mask image;
and reconstructing an oral full-view image corresponding to the oral scanning image based on the dental arch curve and the dental arch thickness.
According to another aspect of the present application, there is provided an oral scanning image processing apparatus, the apparatus comprising:
a alveolar process associated region determining unit configured to determine an alveolar process associated region based on a sagittal projection image corresponding to an oral scan image, the alveolar process associated region including an alveolar process region and at least a part of a mandibular ascending branch region;
an axial projection image obtaining unit, configured to obtain an axial projection image corresponding to the alveolar process association area in the oral scanning image;
the dental arch mask image obtaining unit is used for carrying out image segmentation on the axial projection image to obtain a dental arch mask image;
and a dental arch curve obtaining unit for obtaining a dental arch curve based on the dental arch mask image.
According to another aspect of the present application, there is provided an electronic device comprising a processor and a memory; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method described above.
According to another aspect of the present application, there is provided a computer readable storage medium having stored thereon one or more computer instructions executable by a processor to implement the above-described method.
Compared with the prior art, the application has the following advantages:
according to the oral scanning image processing method, a alveolar process association area is determined based on a sagittal projection image corresponding to an oral scanning image, and the alveolar process association area comprises a mandibular alveolar process area and at least part of a mandibular ascending branch area; obtaining an axial projection image corresponding to a alveolar process association area in an oral scanning image; image segmentation is carried out on the axial projection image to obtain an arch mask image; a dental arch curve is obtained based on the dental arch mask image. The method and the device determine the alveolar process association area based on the sagittal projection image, acquire an axial projection image corresponding to the alveolar process association area, divide the axial projection image to acquire an arch mask image, and acquire a stable and accurate arch mask image under the conditions of CBCT images of small and medium local views, complex tooth conditions (complete mouth missing, mixed dentition, metal artifacts and the like) and acquire a stable and accurate arch curve based on the arch mask image because the arch mask image is acquired in a manner dependent on the alveolar process association area, wherein the alveolar process association area comprises a mandibular alveolar process area and at least a part of mandibular ascending branch area, and is not influenced by view conditions and tooth conditions in an oral cavity.
Further, the application performs distance transformation on the dental arch mask image to obtain a distance transformation image, and uses a skeleton extraction model to divide the distance transformation image to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image; performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image; a dental arch curve is obtained based on the target dental arch skeleton image. The two-stage extraction mode of the dental arch skeleton can obtain a correct target dental arch skeleton when the imaging of teeth and jaw areas is incomplete, so that an accurate dental arch curve is obtained.
Drawings
FIG. 1 is a flowchart of a method for processing an oral scan image according to an embodiment of the present application;
FIG. 2 is a schematic illustration of acquiring a sagittal projection image based on an oral scan image provided in an embodiment of this application;
FIG. 3 is a schematic view of an alveolar process correlation area provided in an embodiment of the present application;
fig. 4 is a schematic diagram of obtaining a mask image of an alveolar process associated area according to an embodiment of the present application;
FIG. 5 is a schematic diagram of obtaining an axial projection image according to an embodiment of the present application;
FIG. 6 is a schematic illustration of obtaining an image of an arch mask provided by an embodiment of the present application;
FIG. 7 is a schematic illustration of a prior art arch skeleton;
FIG. 8 is a schematic illustration of an obtained dental arch framework provided in an embodiment of the present application;
fig. 9 is a schematic view of a panoramic image acquisition procedure according to an embodiment of the present application;
FIG. 10 is a schematic diagram of a local full-view film provided by an embodiment of the present application;
fig. 11 is a block diagram of a unit of an oral scanning image processing apparatus provided in an embodiment of the present application;
fig. 12 is a schematic logic structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is, however, susceptible of embodiment in many other ways than those herein described and similar generalizations can be made by those skilled in the art without departing from the spirit of the application and the application is therefore not limited to the specific embodiments disclosed below.
Aiming at a dental arch curve generation scene and an oral panoramic image reconstruction scene, in order to obtain a stable and accurate dental arch mask image under the conditions of serious tooth deficiency, serious metal artifact and the like, obtain a stable and accurate dental arch curve based on the dental arch mask image, and realize automatic panoramic image reconstruction of an oral scanning image such as CBCT image, CT image and the like under any scene, the application provides an oral scanning image processing method, an oral scanning image processing device corresponding to the method, electronic equipment and a computer readable storage medium. The following provides detailed descriptions of the above methods, apparatuses, electronic devices, and computer-readable storage media.
The present embodiment provides an oral scan image processing method, and an application subject of the method may be applied to a computing device for automatically generating a dental arch curve or automatically reconstructing an oral full-view image. Fig. 1 is a flowchart of a method for processing an oral scan image according to a first embodiment of the present application, and the method provided in this embodiment is described in detail below with reference to fig. 1. The embodiments referred to in the following description are intended to illustrate the method principles and not to limit the practical use.
As shown in fig. 1, the method for processing an oral scan image provided in this embodiment includes the following steps:
s101, determining a alveolar process association area based on a sagittal projection image corresponding to an oral scan image.
The step is used for determining the alveolar process association area based on a sagittal projection image corresponding to an oral scan image, and the oral scan image may be a scan image for an oral cavity such as a CT (Computer Tomography, computerized tomography) image, a CBCT (Cone beam CT) image, or the like. The alveolar process related region includes a mandibular alveolar process region and at least a part of a mandibular elevating branch region (for example, a part of a mandibular region extending to the mandibular elevating branch region) (as shown in fig. 3), and is not a defined anatomical region, and since the alveolar process related region includes the mandibular alveolar process region and a part of the mandibular elevating branch region extending to the mandibular elevating branch region, that is, the region spans a part of the mandibular alveolar process and the mandibular elevating branch, the alveolar process related region is not affected by the tooth condition and the view condition in the oral cavity, for example, in the case of complicated tooth condition (total mouth loss, mixed dentition, metal artifact, etc.), or poor view condition (incomplete jaw display) such as partial small view in the oral cavity, the alveolar process related region can be stably and accurately extracted.
In this embodiment, after a three-dimensional oral scan image is projected according to a sagittal direction by using a maximum intensity projection method (maximum intensity projection, MIP) to obtain a two-dimensional sagittal projection image (as shown in fig. 2), the sagittal projection image is input into a alveolar process correlation region extraction model to obtain a mask image (as shown in fig. 4) of an alveolar process correlation region output by the alveolar process correlation region extraction model, for example, a predetermined number of sagittal projection image samples and corresponding alveolar process correlation region image samples are used as training space books, and model training is performed on a deep neural network model of a network structure such as a U-Net model, a U-net++ model, a deep lab series model, a Transformer, and the like to obtain the aforementioned alveolar process correlation region extraction model for outputting a mask image of an alveolar process correlation region corresponding thereto based on the input sagittal projection image.
S102, obtaining an axial projection image corresponding to the alveolar process association area in the oral scanning image.
After determining the alveolar-related area based on the sagittal projection image corresponding to the oral scan image in the above step, the present step is used to obtain an axial projection image corresponding to the alveolar-related area in the oral scan image, and in this embodiment, the area in the range of the alveolar-related area in the oral scan image may be projected with maximum density according to the axial direction, so as to obtain an axial projection image (as shown in fig. 5).
S103, performing image segmentation on the axial projection image to obtain an arch mask image.
After the axial projection image corresponding to the alveolar process association area is obtained in the above step, the step is used for dividing the axial projection image to obtain an arch mask image. The existing image segmentation method mainly adopts traditional morphological processing methods such as threshold segmentation, however, the traditional morphological processing methods such as threshold segmentation are sensitive to image gray features, segmentation stability and generalization are difficult to ensure, and an object cannot be accurately segmented under a complex scene (for example, an image with serious metal artifacts), in order to overcome the problem, in the embodiment, a depth neural network model is adopted to segment an axial projection image so as to obtain an arch mask image, specifically: as shown in fig. 6, the axial projection image is input into the dental arch mask segmentation model, and a dental arch mask image output by the dental arch mask segmentation model is obtained, and the dental arch mask segmentation model is a pre-trained deep neural network model, for example, a predetermined number of axial projection image samples and dental arch mask image samples corresponding to the axial projection image samples are used as training samples, and model training is performed on the deep neural network model of a network structure such as a U-Net model, a U-net++ model, a deep lab series model, a transducer, and the like, so as to obtain the dental arch mask segmentation model for outputting the corresponding dental arch mask image based on the input axial projection image. By using the depth neural network model to segment the axial projection image, the axial projection image can be stably and accurately segmented under complex scenes such as severe metal artifacts, so as to obtain an accurate dental arch mask image.
S104, obtaining the dental arch curve based on the dental arch mask image.
After the above-described steps of dividing the axial projection image to obtain the arch mask image, the present step is for obtaining an arch curve based on the arch mask image. Currently, a refinement algorithm is mainly adopted to perform skeleton extraction on an arch mask image, and a cubic spline interpolation (cubic spline interpolation) method is used to process the extracted arch skeleton to obtain an arch curve, however, when the above-mentioned oral cavity scanning image is a local view (for example, when the imaging of teeth and jawbone areas in a CBCT image is incomplete), a refinement algorithm is adopted to perform skeleton extraction on the arch mask image, and a correct arch skeleton cannot be obtained (as shown in fig. 7, there is a significant difference between the arch skeleton obtained by the refinement algorithm and an ideal arch skeleton), so the present embodiment adopts the following two-stage skeleton extraction method to perform arch skeleton extraction, and obtain a target arch skeleton image (as shown in fig. 8):
firstly, performing distance transformation (distance transform) on a dental arch mask image to obtain a distance transformation image, specifically, performing distance transformation on the dental arch mask image to obtain a distance value of each point in a dental arch region on the dental arch mask image, which is nearest to a background region, and then performing normalization processing (for example, mapping the distance value to between 0 and 1) on the distance value to obtain the distance transformation image;
secondly, segmenting the distance conversion image by using a skeleton extraction model to obtain an initial dental arch skeleton image (namely a one-stage dental arch skeleton in fig. 8), wherein the skeleton extraction model is a pre-trained deep neural network model, for example, model training is carried out on a U-Net model, a variant of the U-Net model (for example, a U-Net++ model), a deep Lab series model, a transducer and other deep neural network models of network structures by taking a preset number of distance conversion image samples and dental arch skeleton image samples corresponding to the predetermined number of distance conversion image samples as training samples to obtain a skeleton extraction model for outputting a corresponding dental arch skeleton image based on the input distance conversion image; because the dental arch mask image does not have the image characteristics of dental arch skeleton lines, and the distance conversion image contains the image characteristics of basic dental arch skeleton lines, the distance conversion image can enable the automatic extraction process of the dental arch skeleton lines to be learnable, namely, the distance conversion image is used as the input of a skeleton extraction model, and an ideal initial dental arch skeleton can be learned by using the skeleton extraction model;
finally, a refinement algorithm is adopted to carry out secondary skeleton extraction on the initial dental arch skeleton image, and a target dental arch skeleton image (namely the final dental arch skeleton in fig. 8) is obtained.
After the target dental arch skeleton image is obtained in the process, a cubic spline interpolation method is used for processing the target dental arch skeleton image, and a dental arch curve is obtained. Compared with the existing method for extracting the skeleton of the dental arch mask image by adopting a thinning algorithm, the method for extracting the dental arch skeleton in the two stages can obtain the correct target dental arch skeleton when the imaging of the teeth and the jaw bone area is incomplete, so that an accurate dental arch curve is obtained.
According to the embodiment, the alveolar process association area is determined based on the sagittal projection image, the axial projection image corresponding to the alveolar process association area is obtained, the axial projection image is segmented, the dental arch mask image is obtained, and because the dental arch mask image is obtained in a mode depending on the alveolar process association area, the alveolar process association area only comprises a mandibular alveolar process area and at least part of mandibular ascending branch area, the area is not influenced by the visual field condition and the dental condition in the oral cavity, the subsequent dental arch curve obtaining process is not limited by the visual field condition and the dental condition in the oral cavity, and by the mode, stable and accurate dental arch mask images can be obtained under the conditions of CBCT images with local small visual fields and complicated dental conditions (full mouth missing, mixed dental columns, metal artifacts and the like) and stable and accurate dental arch curves are obtained based on the dental arch mask image.
The dental arch thickness may be obtained based on the dental arch mask image before or after the dental arch mask image is obtained, and the dental full-view image corresponding to the oral scan image may be reconstructed based on the dental arch curve and the dental arch thickness after the dental arch mask image is obtained.
In this embodiment, the thickness of the dental arch can be obtained from the distance value obtained by performing the distance transformation on the dental arch mask image in the step S104, for example, the maximum value of the distance transformation represents the farthest value from the background on the dental arch skeleton line, so that the thickness of the dental arch can be approximately 2 times the maximum value of the distance transformation, the thickness of the dental arch is denoted as D, and in consideration of the difference between the upper and lower teeth occlusion relationship and the difference between the inclination angles of the head positions of the film, in order to ensure that the thickness of the dental arch can cover all the teeth, the thickness of the dental arch is enlarged by a predetermined multiple (for example, 1.5 times, i.e., the thickness of the final dental arch is 1.5×d).
In this embodiment, the following conventional method may be specifically used to reconstruct an oral panoramic image (as shown in fig. 9): the above obtained dental arch curve is sampled with equal arc length to obtain N sampling points, the perpendicular line of the dental arch curve at the point is sequentially obtained at each sampling point, the equal distance sampling is carried out along the perpendicular line to two sides to obtain M sampling points, M is equal to the thickness of the dental arch, corresponding pixel values are extracted from oral cavity scanning images (CBCT images, CT images and the like) along the sampling points and are unfolded to obtain M two-dimensional multi-planar reconstruction (MPR) images, and the M images are synthesized to obtain a panoramic image, in this embodiment, the following formula can be adopted for synthesis:
wherein X is m Represents the mth MPR image, M takes values 1 to M, and Y represents the panorama image. In order to further improve the image contrast of the synthesized full-scene Y, the invention adopts a Unsharp Mask (USM) sharpening algorithm, and the formula is as follows:
Z＝Y+α(Y-G(Y))
where G represents a gaussian filter function, α represents a weighting factor (which may be set to 2), and Z represents the final oral full view image (as shown in fig. 9).
Panoramic film automatic reconstruction based on oral scanning images is indispensable for image analysis and diagnosis. At present, most software and products need to manually draw dental archwire to realize the reconstruction of the oral panoramic film image, and a small part of software can realize the automatic reconstruction of the oral panoramic film image, but the application scene is limited and the effect is poor. The current automatic reconstruction method of the full-view image of the oral cavity aims at simple scenes such as complete visual field (namely, jaw bone in an oral cavity scanning image is basically displayed), good tooth condition (namely, no obvious missing of teeth) and the like, and can not automatically reconstruct the available full-view image of the oral cavity for scenes such as the oral cavity scanning image (CBCT image, CT image and the like) with small and small local visual field and the scene with complex tooth condition (full-mouth missing, mixed dentition, metal artifact and the like).
In order to solve the above-described drawbacks of the related art, by implementing the above-described oral scan image processing method provided by the present embodiment, the manner of obtaining an arch mask image depends on an area associated with an alveolus, the area associated with an alveolus includes an area of an alveolus and an area of at least a part of a mandibular ascending branch, and the area is not affected by the view condition and the tooth condition in the oral cavity, so that the process of obtaining an arch curve is not limited by the view condition and the tooth condition in the oral cavity, and by this manner, a stable and accurate arch mask image can be obtained in a partial small-view oral scan image, a complex tooth condition (total mouth missing, mixed dentition, metal artifact, etc.), and a stable and accurate arch curve can be obtained based on the arch mask image, and then based on the arch curve, an automatic reconstruction of a piece of an oral scan image such as a CBCT image, a CT image, etc. covering an oral scan image in any scene (including a partial small-view CBCT image, CT image, etc., a complex tooth condition (total mouth missing, mixed dentition, metal artifact, etc.) can be realized as shown in fig. 10).
The foregoing embodiments provide a method for processing an oral scan image, and correspondingly, another embodiment of the present application further provides an oral scan image processing apparatus, and since the apparatus embodiments are substantially similar to the method embodiments, the description of the apparatus embodiments is relatively simple, and details of relevant technical features should be referred to the corresponding descriptions of the method embodiments provided above, and the following descriptions of the apparatus embodiments are merely illustrative.
Referring to fig. 11 for an understanding of the present embodiment, fig. 11 is a block diagram of a unit of an oral scanning image processing apparatus according to the present embodiment, and as shown in fig. 11, the apparatus according to the present embodiment includes:
a alveolar process associated region determining unit 201 configured to determine an alveolar process associated region based on a sagittal projection image corresponding to an oral scan image, the alveolar process associated region including a mandibular alveolar process region and at least a part of a mandibular ascending branch region;
an axial projection image obtaining unit 202, configured to obtain an axial projection image corresponding to the alveolar process association area in the oral scanning image;
a dental arch mask image obtaining unit 203, configured to segment the axial projection image to obtain a dental arch mask image;
a dental arch curve obtaining unit 204 for obtaining a dental arch curve based on the dental arch mask image.
In some embodiments, the obtaining an arch curve based on the arch mask image comprises:
performing distance transformation on the dental arch mask image to obtain a distance transformation image,
dividing the distance transformation image by using a skeleton extraction model to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image;
performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image;
and obtaining an arch curve based on the target arch skeleton image.
In some embodiments, the obtaining an arch curve based on the target arch skeleton image comprises: and processing the target dental arch skeleton image by using a cubic spline interpolation method to obtain the dental arch curve.
In some embodiments, the determining the alveolar process correlation area based on the sagittal projection image corresponding to the oral scan image includes: inputting the sagittal projection image corresponding to the oral cavity scanning image into a alveolar process association area extraction model to obtain a mask image of the alveolar process association area output by the alveolar process association area extraction model, wherein the alveolar process association area extraction model is a pre-trained deep neural network model for outputting the mask image of the corresponding alveolar process association area based on the input sagittal projection image.
In some embodiments, the obtaining an axial projection image corresponding to the alveolar process association area in the oral scan image includes: and carrying out maximum density projection on the area in the range of the alveolar process association area in the oral cavity scanning image according to the axial direction to obtain the axial projection image.
In some embodiments, the image segmentation of the axial projection image to obtain an arch mask image includes: inputting the axial projection image into a dental arch mask segmentation model to obtain the dental arch mask image output by the dental arch mask segmentation model, wherein the dental arch mask segmentation model is a pre-trained deep neural network model for outputting a corresponding dental arch mask image based on the input axial projection image.
In some embodiments, the apparatus further comprises:
a dental arch thickness obtaining unit for obtaining a dental arch thickness based on the dental arch mask image;
and the panoramic image reconstruction unit is used for reconstructing an oral cavity full-view image corresponding to the oral cavity scanning image based on the dental arch curve and the dental arch thickness.
According to the oral scanning image processing device, the alveolar process associated area is determined based on the sagittal projection image, the axial projection image corresponding to the alveolar process associated area is obtained, the axial projection image is segmented, the dental arch mask image is obtained, and because the dental arch mask image is obtained in a mode depending on the alveolar process associated area, the alveolar process associated area only comprises the alveolar process area and at least part of mandibular ascending branch area, the area is not affected by the visual field condition and the tooth condition in an oral cavity, the subsequent dental arch curve obtaining process is free from the constraint of the visual field condition and the tooth condition in the oral cavity, the stable and accurate dental arch mask image can be obtained under the conditions of serious tooth loss and serious metal artifact by the mode, and the stable and accurate dental arch curve can be obtained based on the dental arch mask image.
In the foregoing embodiments, an oral scan image processing method and an oral scan image processing apparatus are provided, and in addition, another embodiment of the present application further provides an electronic device, and since the electronic device embodiment is substantially similar to the method embodiment, the description is relatively simple, and details of relevant technical features should be referred to the corresponding description of the method embodiment provided above, and the following description of the electronic device embodiment is merely illustrative. The electronic device embodiment is as follows:
fig. 12 is a schematic diagram of an electronic device according to the present embodiment.
As shown in fig. 12, the electronic device provided in this embodiment includes: a processor 301 and a memory 302;
the memory 302 is used to store computer instructions for data processing which, when read and executed by the processor 301, perform the following operations:
determining a alveolar process associated region based on a sagittal projection image corresponding to the oral scan image, the alveolar process associated region including an alveolar process region and at least a portion of a mandibular ascending branch region;
obtaining an axial projection image corresponding to the alveolar process association area in the oral scanning image;
image segmentation is carried out on the axial projection image to obtain an arch mask image;
and obtaining a dental arch curve based on the dental arch mask image.
In some embodiments, the obtaining an arch curve based on the arch mask image comprises:
performing distance transformation on the dental arch mask image to obtain a distance transformation image,
dividing the distance transformation image by using a skeleton extraction model to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image;
performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image;
and obtaining an arch curve based on the target arch skeleton image.
In some embodiments, the obtaining an arch curve based on the target arch skeleton image comprises:
and processing the target dental arch skeleton image by using a cubic spline interpolation method to obtain the dental arch curve.
In some embodiments, the determining the alveolar process correlation area based on the sagittal projection image corresponding to the oral scan image includes:
inputting the sagittal projection image corresponding to the oral cavity scanning image into a alveolar process association area extraction model to obtain a mask image of the alveolar process association area output by the alveolar process association area extraction model, wherein the alveolar process association area extraction model is a pre-trained deep neural network model for outputting the mask image of the corresponding alveolar process association area based on the input sagittal projection image.
In some embodiments, the obtaining an axial projection image corresponding to the alveolar process association area in the oral scan image includes:
and carrying out maximum density projection on the area in the range of the alveolar process association area in the oral cavity scanning image according to the axial direction to obtain the axial projection image.
In some embodiments, the image segmentation of the axial projection image to obtain an arch mask image includes:
inputting the axial projection image into a dental arch mask segmentation model to obtain the dental arch mask image output by the dental arch mask segmentation model, wherein the dental arch mask segmentation model is a pre-trained deep neural network model for outputting a corresponding dental arch mask image based on the input axial projection image.
In some embodiments, further comprising:
obtaining a dental arch thickness based on the dental arch mask image;
and reconstructing an oral full-view image corresponding to the oral scanning image based on the dental arch curve and the dental arch thickness.
By using the electronic device provided by the embodiment, the alveolar process associated area can be determined based on the sagittal projection image, the axial projection image corresponding to the alveolar process associated area is obtained, the axial projection image is segmented, the dental arch mask image is obtained, and because the dental arch mask image is obtained in a manner dependent on the alveolar process associated area, the alveolar process associated area only comprises the alveolar process area and at least part of the mandibular ascending branch area, the area is not influenced by the view condition and the tooth condition in the oral cavity, the subsequent dental arch curve obtaining process is not limited by the view condition and the tooth condition in the oral cavity, and by the manner, the stable and accurate dental arch mask image can be obtained under the conditions of serious tooth loss and serious metal artifact, and the stable and accurate dental arch curve can be obtained based on the dental arch mask image.
In the above-described embodiments, an oral scan image processing method, an oral scan image processing apparatus, and an electronic device are provided, and in addition, another embodiment of the present application further provides a computer-readable storage medium for implementing the above-described oral scan image processing method. The embodiments of the computer readable storage medium provided in the present application are described more simply, and reference should be made to the corresponding descriptions of the above-described method embodiments, the embodiments described below being merely illustrative.
The computer readable storage medium provided in this embodiment stores computer instructions that, when executed by a processor, implement the steps of:
determining a alveolar process associated region based on a sagittal projection image corresponding to the oral scan image, the alveolar process associated region including an alveolar process region and at least a portion of a mandibular ascending branch region;
obtaining an axial projection image corresponding to the alveolar process association area in the oral scanning image;
image segmentation is carried out on the axial projection image to obtain an arch mask image;
and obtaining a dental arch curve based on the dental arch mask image.
In some embodiments, the obtaining an arch curve based on the arch mask image comprises:
performing distance transformation on the dental arch mask image to obtain a distance transformation image,
dividing the distance transformation image by using a skeleton extraction model to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image;
performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image;
and obtaining an arch curve based on the target arch skeleton image.
In some embodiments, the obtaining an arch curve based on the target arch skeleton image comprises:
and processing the target dental arch skeleton image by using a cubic spline interpolation method to obtain the dental arch curve.
In some embodiments, the determining the alveolar process correlation area based on the sagittal projection image corresponding to the oral scan image includes:
inputting the sagittal projection image corresponding to the oral cavity scanning image into a alveolar process association area extraction model to obtain a mask image of the alveolar process association area output by the alveolar process association area extraction model, wherein the alveolar process association area extraction model is a pre-trained deep neural network model for outputting the mask image of the corresponding alveolar process association area based on the input sagittal projection image.
In some embodiments, the obtaining an axial projection image corresponding to the alveolar process association area in the oral scan image includes:
and carrying out maximum density projection on the area in the range of the alveolar process association area in the oral cavity scanning image according to the axial direction to obtain the axial projection image.
In some embodiments, the image segmentation of the axial projection image to obtain an arch mask image includes:
inputting the axial projection image into a dental arch mask segmentation model to obtain the dental arch mask image output by the dental arch mask segmentation model, wherein the dental arch mask segmentation model is a pre-trained deep neural network model for outputting a corresponding dental arch mask image based on the input axial projection image.
In some embodiments, further comprising:
obtaining a dental arch thickness based on the dental arch mask image;
and reconstructing an oral full-view image corresponding to the oral scanning image based on the dental arch curve and the dental arch thickness.
By executing the computer instructions stored on the computer readable storage medium provided in this embodiment, the alveolar process associated area can be determined based on the sagittal projection image, and an axial projection image corresponding to the alveolar process associated area can be obtained, the axial projection image is segmented, and the dental arch mask image is obtained, and since the dental arch mask image is obtained in a manner dependent on the alveolar process associated area, the alveolar process associated area only includes the alveolar process area and at least part of the mandibular ascending branch area, the area is not affected by the view condition and the tooth condition in the oral cavity, so that the subsequent process of obtaining the dental arch curve is not constrained by the view condition and the tooth condition in the oral cavity, by this manner, a stable and accurate dental arch mask image can be obtained under the conditions of serious tooth loss and serious metal artifact, and a stable and accurate dental arch curve can be obtained based on the dental arch mask image.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
1. Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include non-transitory computer-readable media (trans itory med ia), such as modulated data signals and carrier waves.
2. It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
While the preferred embodiment has been described, it is not intended to limit the invention thereto, and any person skilled in the art may make variations and modifications without departing from the spirit and scope of the present invention, so that the scope of the present invention shall be defined by the claims of the present application.
Claims (10)
1. A method of processing an oral scan image, comprising:
determining a alveolar process associated region based on a sagittal projection image corresponding to the oral scan image, the alveolar process associated region including a mandibular alveolar process region and at least a portion of a mandibular ascending branch region;
obtaining an axial projection image corresponding to the alveolar process association area in the oral scanning image;
image segmentation is carried out on the axial projection image to obtain an arch mask image;
and obtaining a dental arch curve based on the dental arch mask image.
2. The method of claim 1, wherein the obtaining an arch curve based on the arch mask image comprises:
performing distance transformation on the dental arch mask image to obtain a distance transformation image,
dividing the distance transformation image by using a skeleton extraction model to obtain an initial dental arch skeleton image, wherein the skeleton extraction model is a pre-trained deep neural network model for outputting a corresponding dental arch skeleton image based on the input distance transformation image;
performing secondary skeleton extraction on the initial dental arch skeleton image by adopting a refinement algorithm to obtain a target dental arch skeleton image;
and obtaining an arch curve based on the target arch skeleton image.
3. The method of claim 2, wherein the obtaining an arch curve based on the target arch skeleton image comprises:
and processing the target dental arch skeleton image by using a cubic spline interpolation method to obtain the dental arch curve.
4. The method of claim 1, wherein the determining the alveolar process correlation area based on the sagittal projection image corresponding to the oral scan image comprises:
inputting the sagittal projection image corresponding to the oral cavity scanning image into a alveolar process association area extraction model to obtain a mask image of the alveolar process association area output by the alveolar process association area extraction model, wherein the alveolar process association area extraction model is a pre-trained deep neural network model for outputting the mask image of the corresponding alveolar process association area based on the input sagittal projection image.
5. The method of claim 1, wherein the obtaining an axial projection image corresponding to the alveolar process correlation area in the oral scan image comprises:
and carrying out maximum density projection on the area in the range of the alveolar process association area in the oral cavity scanning image according to the axial direction to obtain the axial projection image.
6. The method of claim 1, wherein the image segmentation of the axial projection image to obtain an arch mask image comprises:
inputting the axial projection image into a dental arch mask segmentation model to obtain the dental arch mask image output by the dental arch mask segmentation model, wherein the dental arch mask segmentation model is a pre-trained deep neural network model for outputting a corresponding dental arch mask image based on the input axial projection image.
7. The method according to claim 1, wherein the method further comprises:
obtaining a dental arch thickness based on the dental arch mask image;
and reconstructing an oral full-view image corresponding to the oral scanning image based on the dental arch curve and the dental arch thickness.
8. An oral scan image processing apparatus, the apparatus comprising:
a alveolar process association area determining unit configured to determine an alveolar process association area based on a sagittal projection image corresponding to an oral scan image, the alveolar process association area including a mandibular alveolar process area and at least a part of a mandibular ascending branch area;
an axial projection image obtaining unit, configured to obtain an axial projection image corresponding to the alveolar process association area in the oral scanning image;
the dental arch mask image obtaining unit is used for carrying out image segmentation on the axial projection image to obtain a dental arch mask image;
and a dental arch curve obtaining unit for obtaining a dental arch curve based on the dental arch mask image.
9. An electronic device comprising a processor and a memory; wherein,,
the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executable by the processor to implement the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon one or more computer instructions executable by a processor to implement the method of any of claims 1-7.
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| CN202310210418.4A CN116309909A (en) | 2023-03-07 | 2023-03-07 | Oral cavity scanning image processing method and device |
| PCT/CN2023/088972 WO2024183133A1 (en) | 2023-03-07 | 2023-04-18 | Oral scanning image processing method and apparatus |
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| CN202310210418.4A CN116309909A (en) | 2023-03-07 | 2023-03-07 | Oral cavity scanning image processing method and device |
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| CN117291281A (en) * | 2023-08-29 | 2023-12-26 | 强联智创（北京）科技有限公司 | Method for training evaluation model for evaluating brain collateral circulation and related product |
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| US20140329194A1 (en) * | 2013-05-05 | 2014-11-06 | Rohit Sachdeva | Orthodontic treatment planning using biological constraints |
| CN105608747B (en) * | 2015-12-17 | 2018-11-06 | 首都师范大学 | A method of from three-dimensional dentistry conical beam CT extracting data panorama sketch |
| CN106214175B (en) * | 2016-08-26 | 2020-06-16 | 中国科学院深圳先进技术研究院 | Method and device for estimating three-dimensional tooth axis |
| CN106846307B (en) * | 2017-01-19 | 2020-09-22 | 深圳市深图医学影像设备有限公司 | Image processing method and device based on cone beam computed tomography |
| CN110211200B (en) * | 2019-04-22 | 2023-04-18 | 深圳安科高技术股份有限公司 | Dental arch wire generating method and system based on neural network technology |
| CN110236673B (en) * | 2019-06-10 | 2021-04-09 | 北京大学口腔医学院 | Database-based preoperative design method and device for reconstruction of bilateral jaw defects |
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| CN117291281A (en) * | 2023-08-29 | 2023-12-26 | 强联智创（北京）科技有限公司 | Method for training evaluation model for evaluating brain collateral circulation and related product |
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