CN114782345A - Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium - Google Patents
Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium Download PDFInfo
- Publication number
- CN114782345A CN114782345A CN202210383604.3A CN202210383604A CN114782345A CN 114782345 A CN114782345 A CN 114782345A CN 202210383604 A CN202210383604 A CN 202210383604A CN 114782345 A CN114782345 A CN 114782345A
- Authority
- CN
- China
- Prior art keywords
- dimensional
- lesion
- tooth
- oral cavity
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/10—Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30036—Dental; Teeth
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Graphics (AREA)
- Image Processing (AREA)
- Dental Tools And Instruments Or Auxiliary Dental Instruments (AREA)
Abstract
The embodiment of the disclosure relates to an oral cavity detection method, an oral cavity detection device, an electronic device and a medium based on artificial intelligence, wherein the method comprises the following steps: the method comprises the steps of obtaining a two-dimensional texture map corresponding to a three-dimensional tooth model, processing the two-dimensional texture map based on a pre-trained oral cavity detection model to obtain a lesion area, and back-projecting the lesion area to the three-dimensional tooth model to obtain a lesion position of the three-dimensional tooth model. By adopting the technical scheme, the problem that the lesion area is omitted is avoided by detecting the two-dimensional texture map, the lesion area is identified based on the oral detection model trained in advance, the identification precision of the lesion area is improved, the lesion position is presented in a three-dimensional model mode, the presentation effect is more visual, and the detection efficiency and effect under the oral detection scene are further improved.
Description
Technical Field
The present disclosure relates to the field of intelligent oral medicine technologies, and in particular, to an oral cavity detection method, apparatus, electronic device, and medium based on artificial intelligence.
Background
With the continuous development and progress of society and the continuous improvement of living standard of people, people pay more and more attention to the situation of oral teeth.
In the related art, oral cavity diseases are identified and judged by an oral doctor according to own experience, the oral doctor needs to have certain clinical experience, and the oral doctor can consume a great deal of energy, and in addition, the diseased region is detected by mistake and is missed.
Disclosure of Invention
To solve the above technical problems or to at least partially solve the above technical problems, the present disclosure provides an oral cavity detection method, apparatus, electronic device and medium based on artificial intelligence.
The embodiment of the present disclosure provides an oral cavity detection method based on artificial intelligence, the method includes:
acquiring a two-dimensional texture map corresponding to the three-dimensional tooth model;
processing the two-dimensional texture map based on a pre-trained oral detection model to obtain a lesion area;
and back projecting the lesion area to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model.
The disclosed embodiment also provides an oral cavity detection device based on artificial intelligence, the device includes:
the image acquisition module is used for acquiring a two-dimensional texture map corresponding to the three-dimensional tooth model;
the processing module is used for processing the two-dimensional texture map based on a pre-trained oral detection model to obtain a lesion area;
and the back projection module is used for back projecting the lesion area to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; the processor is used for reading the executable instructions from the memory and executing the instructions to realize the artificial intelligence based oral cavity detection method provided by the embodiment of the disclosure.
The disclosed embodiments also provide a computer-readable storage medium storing a computer program for executing the artificial intelligence based oral cavity detection method provided by the disclosed embodiments.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages: according to the oral cavity detection scheme based on artificial intelligence, the two-dimensional texture map corresponding to the three-dimensional tooth model is obtained, the two-dimensional texture map is processed based on the oral cavity detection model trained in advance to obtain a lesion area, and the lesion area is back projected to the three-dimensional tooth model to obtain a lesion position of the three-dimensional tooth model. By adopting the technical scheme, the information of the whole set of teeth can be completely observed by detecting the two-dimensional texture map corresponding to the three-dimensional tooth model in the oral cavity detection process, the problem that the lesion area is omitted due to improper selection of the observation visual angle of the three-dimensional tooth model is avoided, and the lesion area is identified based on the oral cavity detection model trained in advance, so that the identification precision of the lesion area can be greatly improved, in addition, the lesion position can be presented in a three-dimensional model manner, the presentation effect is more visual, and the detection efficiency and the detection effect under the oral cavity detection scene are further improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of an artificial intelligence-based oral cavity detection method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another artificial intelligence-based oral cavity detection method provided by the embodiment of the present disclosure;
FIG. 3a is a schematic view of a three-dimensional tooth model provided by an embodiment of the present disclosure;
FIG. 3b is a schematic diagram of a two-dimensional texture map provided by an embodiment of the present disclosure;
FIG. 3c is a schematic diagram of another two-dimensional texture map provided by an embodiment of the present disclosure;
FIG. 3d is a schematic diagram of another two-dimensional texture map provided by an embodiment of the present disclosure;
FIG. 4 is a schematic view of another three-dimensional tooth model provided by an embodiment of the present disclosure;
FIG. 5 is a schematic view of yet another three-dimensional tooth model provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an artificial intelligence-based oral cavity detection device according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present disclosure. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein is intended to be open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a" or "an" in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will appreciate that references to "one or more" are intended to be exemplary and not limiting unless the context clearly indicates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
In practical application, in the detection and identification process of common oral diseases such as decayed teeth, dental calculus and pigmentation, an oral doctor needs to identify a lesion part, the oral doctor needs to have certain clinical experience, and a great deal of energy of the doctor is consumed; in addition, when the lesion site is identified, the wrong selection of the observation angle of the dentist may cause false detection and missed detection of the lesion site.
In order to solve the problems, the oral cavity detection method based on artificial intelligence is provided, the two-dimensional texture map corresponding to the three-dimensional tooth model is obtained, the two-dimensional texture map is processed based on the oral cavity detection model trained in advance to obtain a lesion area, the lesion area is back projected to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model, and the lesion position is rapidly and accurately positioned and identified in an automatic mode.
Specifically, fig. 1 is a schematic flowchart of an artificial intelligence based oral cavity detection method provided by an embodiment of the present disclosure, which may be executed by an artificial intelligence based oral cavity detection apparatus, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 1, the method includes:
step 101, obtaining a two-dimensional texture map corresponding to the three-dimensional tooth model.
The three-dimensional tooth model may be any one of three-dimensional tooth models, and the source of the three-dimensional tooth model is not limited in the embodiment of the present disclosure, for example, the three-dimensional tooth model may be obtained by scanning the upper row of teeth or the lower row of teeth of the human mouth in real time by a scanning device, or may be a three-dimensional tooth model corresponding to the upper row of teeth or the lower row of teeth, which is obtained by sending based on a download address or other devices. The two-dimensional texture image is a two-dimensional plane texture image obtained by carrying out network parameterization expansion on the three-dimensional tooth model, so that the information of the whole pair of teeth can be observed completely, and the problem that a lesion area is omitted due to improper observation visual angle selection is avoided.
In some embodiments, the three-dimensional tooth model is subjected to network parameterization to obtain the two-dimensional texture map, for example, a plurality of three-dimensional vertexes corresponding to the three-dimensional tooth model are obtained, a plurality of triangles are obtained based on the three-dimensional vertexes, a linear equation set is established based on the included angle between two adjacent sides of each triangle and the ratio of the two sides, the linear equation set is solved based on preset initial conditions, and the two-dimensional texture map is constructed based on the two-dimensional parameters.
In other embodiments, a plurality of three-dimensional coordinate points corresponding to the three-dimensional tooth model are obtained, the three-dimensional coordinate points are converted based on the dimension conversion model to obtain a plurality of two-dimensional coordinate points, and the two-dimensional texture map is constructed based on the two-dimensional coordinate points. The above two manners are only examples of obtaining the two-dimensional texture map corresponding to the three-dimensional tooth model, and the embodiment of the present disclosure does not limit the specific manner of obtaining the two-dimensional texture map corresponding to the three-dimensional tooth model.
Specifically, after the three-dimensional tooth model is obtained, the three-dimensional tooth model is composed of a plurality of three-dimensional vertexes, and the mapping relation between the three-dimensional vertex coordinates on the three-dimensional tooth model and the two-dimensional coordinates of the parameter domain plane is obtained, so that the three-dimensional tooth model is flattened into a two-dimensional plane texture picture.
And 102, processing the two-dimensional texture map based on a pre-trained oral cavity detection model to obtain a lesion area.
The oral cavity detection model is a pre-trained oral cavity detection model for lesion region identification, and the detailed description of the following embodiments of the specific training process is omitted here. The lesion area refers to an area corresponding to a dental lesion such as caries, dental calculus, pigmentation, or the like on the two-dimensional texture map.
In the embodiments of the present disclosure, there are many ways to obtain a lesion region by processing the two-dimensional texture map based on a pre-trained oral cavity detection model, and in some embodiments, the two-dimensional texture map is detected based on a target detection network in the oral cavity detection model to obtain a plurality of tooth regions, each tooth region is identified based on a semantic segmentation network in the oral cavity detection model to obtain an identification result of each tooth region, and the lesion region is determined based on the identification result of each tooth region.
In other embodiments, feature extraction is performed on the two-dimensional texture map based on the oral cavity detection model to obtain a plurality of feature values, each feature value is compared with a preset feature threshold, and a lesion region is determined based on the comparison result. The above two modes are only examples of processing the two-dimensional texture map based on the oral cavity detection model trained in advance to obtain the lesion area, and the embodiment of the present disclosure does not limit the specific mode of processing the two-dimensional texture map based on the oral cavity detection model trained in advance to obtain the lesion area.
In the embodiment of the present disclosure, after the two-dimensional texture map is obtained, the two-dimensional texture map may be processed based on a pre-trained oral cavity detection model to obtain a lesion region.
And 103, back projecting the lesion area to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model.
The lesion area refers to an area corresponding to a dental lesion such as dental caries, dental calculus, pigmentation and the like on the two-dimensional texture map, and therefore the lesion area needs to be back-projected to the three-dimensional tooth model to obtain a lesion position of the three-dimensional tooth model. Wherein, the lesion position refers to the position corresponding to the dental lesion such as decayed tooth, dental calculus, pigmentation and the like on the three-dimensional dental model.
In some embodiments, a two-dimensional coordinate point corresponding to the lesion region is obtained, a three-dimensional coordinate point corresponding to the two-dimensional coordinate point is obtained based on a prestored dimension coordinate mapping relation table, and the lesion position of the three-dimensional tooth model is determined based on the three-dimensional coordinate point.
In other embodiments, coordinate points projected by the three-dimensional tooth model onto the two-dimensional plane are obtained, a target coordinate point matching the lesion region is obtained, and the target coordinate point is reflected to the position of the three-dimensional tooth model as the lesion position. The above two manners are only examples of obtaining the lesion position of the three-dimensional tooth model by back-projecting the lesion region to the three-dimensional tooth model, and the embodiment of the present disclosure does not limit the specific manner of obtaining the lesion position of the three-dimensional tooth model by back-projecting the lesion region to the three-dimensional tooth model.
Specifically, after the lesion area is obtained, the lesion area can be back-projected to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model, and the lesion position can be presented in a three-dimensional model manner, so that the presentation effect is more visual.
According to the oral cavity detection scheme based on artificial intelligence, the two-dimensional texture map corresponding to the three-dimensional tooth model is obtained, the two-dimensional texture map is processed based on the oral cavity detection model trained in advance to obtain a lesion area, and the lesion area is back projected to the three-dimensional tooth model to obtain a lesion position of the three-dimensional tooth model. By adopting the technical scheme, the information of the whole set of teeth can be completely observed by detecting the two-dimensional texture map corresponding to the three-dimensional tooth model in the oral cavity detection process, the problem that the lesion area is omitted due to improper selection of the observation visual angle of the three-dimensional tooth model is avoided, and the lesion area is identified based on the oral cavity detection model trained in advance, so that the identification precision of the lesion area can be greatly improved, in addition, the lesion position can be presented in a three-dimensional model manner, the presentation effect is more visual, and the detection efficiency and the detection effect under the oral cavity detection scene are further improved.
Based on the description of the embodiment, through mesh parameterization expansion, detect the information that can observe whole set of tooth completely with the mode of two-dimensional texture map, avoided three-dimensional model because the improper problem that leads to the lesion region to be omitted of observation visual angle selection, and can high-efficiently accurately discern the lesion problem through the deep learning technique, and through the mode of back projection back to three-dimensional mesh with the discernment result of two-dimensional texture map after the grid parameter expansion, can present the lesion position with the mode of three-dimensional model, make the presentation effect more directly perceived, calculate the observation visual angle that non-sheltered from according to the lesion position at last, can make things convenient for the dentist to fix a position and count the lesion position fast, effectively avoid omitting.
It can be understood that, based on the deep learning network using two stages of detection and segmentation, the tooth region is detected in the two-dimensional texture map first, and then the segmentation recognition of the lesion problem is performed on the tooth region, which can further improve the recognition accuracy of small lesion regions such as caries and how to train the oral cavity detection model, and correlate the observation perspective of the screenshot to the observation perspective of the three-dimensional tooth model, further facilitate the stomatologist to quickly locate and count the lesion position, which is described in detail below with reference to fig. 2.
Specifically, fig. 2 is a schematic flow chart of another artificial intelligence-based oral cavity detection method according to an embodiment of the present disclosure, and the present embodiment further optimizes the artificial intelligence-based oral cavity detection method based on the above embodiment. As shown in fig. 2, the method includes:
step 201, performing network parameterization processing on the three-dimensional tooth model to obtain a two-dimensional texture map.
Specifically, a plurality of three-dimensional vertexes corresponding to the three-dimensional tooth model are obtained, a plurality of triangles are obtained based on the three-dimensional vertexes, a linear equation set is established based on the included angle of two adjacent sides of each triangle and the ratio of the two sides, the linear equation set is solved based on preset initial conditions, and a two-dimensional texture map is established based on two-dimensional parameters and two-dimensional parameters.
Specifically, the three-dimensional tooth model is composed of a plurality of three-dimensional vertexes, and the mapping relation between three-dimensional vertex coordinates on the three-dimensional tooth model and two-dimensional plane coordinates of a parameter domain plane is obtained, so that the three-dimensional tooth model is flattened into a two-dimensional texture map.
In order to rapidly solve and reduce the deformation distortion of a triangle on a three-dimensional tooth model caused by parameterization, a linear equation set is established through the included angle between two adjacent sides of the triangle and the proportional value of the two sides, and the linear equation set can be rapidly solved after initial conditions are given, so that a parameterized two-dimensional texture map is obtained.
The preset initial condition is a condition that is constrained to solving, for example, it is required to ensure that three included angles of a triangle are kept as unchanged as possible, for example, it is required to ensure that an expanded two-dimensional texture map is as compact as possible, and for example, it is required to ensure that time for expanding the two-dimensional texture map is as short as possible, and the preset initial condition is specifically set according to application scene requirements. In the embodiment of the disclosure, the angles of the triangular patches are kept consistent as much as possible, so that teeth on the unfolded two-dimensional texture map are not distorted, thereby influencing the subsequent recognition and further improving the accuracy of oral cavity detection.
For example, fig. 3a is a schematic diagram of a three-dimensional tooth model provided in an embodiment of the present disclosure, fig. 3a illustrates an original three-dimensional tooth model, a two-dimensional texture map corresponding to the three-dimensional tooth model is obtained as shown in fig. 3b, and fig. 3b illustrates a schematic diagram of the obtained two-dimensional texture map.
Step 202, obtaining two-dimensional texture map samples corresponding to the three-dimensional tooth model samples; the two-dimensional texture map sample comprises a marked tooth area and a marked lesion area, the two-dimensional texture map sample is input into a target detection network for detection to obtain a training tooth area, and the training tooth area is input into a semantic segmentation network for recognition to obtain a training lesion area.
Step 203, adjusting network parameters of the target detection network based on the first comparison results of the training tooth area and the marking tooth area, and adjusting network parameters of the semantic segmentation network based on the second comparison results of the training lesion area and the marking lesion area to obtain an oral cavity detection model.
In the embodiment of the disclosure, the labeled two-dimensional texture map forms a training sample, the target detection network identifies the position of the tooth in the two-dimensional texture map so as to obtain each tooth region, the semantic segmentation network classifies each pixel point in the image corresponding to the tooth region, and determines whether the category of each pixel point belongs to the lesion region.
In the embodiment of the disclosure, network parameters of the target detection network are adjusted based on the first comparison results of the training tooth area and the marking tooth area, and network parameters of the semantic segmentation network are adjusted based on the second comparison results of the training lesion area and the marking lesion area, and the target detection network and the semantic segmentation network are continuously optimized until the loss value is smaller than a preset threshold value, so as to obtain the oral cavity detection model.
And 204, detecting the two-dimensional texture map based on a target detection network in the oral cavity detection model to obtain a plurality of tooth areas, identifying each tooth area based on a semantic segmentation network in the oral cavity detection model to obtain an identification result of each tooth area, and determining a lesion area based on the identification result of each tooth area.
Specifically, the target detection network detects a single tooth area, the semantic segmentation network identifies a lesion area of the single tooth area, and the lesion area in the single tooth area is overlaid on the original two-dimensional texture map again.
Illustratively, taking the two-dimensional texture map in fig. 3b as an example, fig. 3c is a schematic diagram of another two-dimensional texture map provided by the embodiment of the present disclosure, and fig. 3c shows tooth regions obtained by performing object detection, and each tooth region is identified based on a semantic segmentation network in an oral cavity detection model, such as the lesion region 11 shown in fig. 3 d.
Specifically, the small boxes in fig. 3c are obtained by the target detection technique of deep learning, and a plurality of boxes are used to select the tooth region, so that attention can be focused on a single region (i.e. the subsequent segmentation network only processes image data in a single box), and a fine lesion region such as caries can be identified more accurately than the segmentation processing of the entire expansion map.
In addition, a single tooth area is detected based on the deep learning network, only the tooth area is identified, and compared with the whole image, a finer lesion area can be identified.
And 205, acquiring a two-dimensional coordinate point corresponding to the lesion area, acquiring a three-dimensional coordinate point corresponding to the two-dimensional coordinate point based on a prestored dimension coordinate mapping relation table, and determining the lesion position of the three-dimensional tooth model based on the three-dimensional coordinate point.
Specifically, when the three-dimensional tooth model is expanded into the two-dimensional texture map, the one-to-one correspondence between the vertex of the three-dimensional tooth model and the coordinates of the two-dimensional points is stored, and when the two-dimensional texture map identifies that a certain point is a lesion, the coordinates of the corresponding three-dimensional point can be inquired.
Illustratively, taking the two-dimensional texture map in fig. 3d as an example, the lesion region 11 is projected to a three-dimensional tooth model, as shown in fig. 4, showing a lesion position 21 of the three-dimensional tooth model.
And step 206, calculating an observation visual angle based on the lesion position, and acquiring and displaying a target picture based on the observation visual angle.
Wherein, the observation visual angle refers to the visual angle for observing the lesion position without shielding.
In some embodiments, an average normal of the lesion position is obtained, two adjacent tooth positions are determined based on the lesion position, a target linear direction is determined based on the two adjacent tooth positions, a target included angle is determined based on the average normal and the target linear direction, and the observation visual angle is determined based on the target included angle and a preset angle threshold.
Specifically, the non-shielded observation visual angle is calculated according to the lesion position, the target picture is acquired based on the observation visual angle and displayed, the target picture of the specific lesion position is given, a dentist can conveniently and quickly position and count the lesion position, and omission is effectively avoided.
And step 207, carrying out association processing on the observation visual angle and the visual angle of the three-dimensional tooth model.
For example, fig. 5 is a schematic diagram of another three-dimensional tooth model provided in the embodiment of the present disclosure, and the tooth position number of each tooth may be obtained through an automatic or manual marking manner, such as numbers 1-16 in fig. 5, assuming that the diseased position is the position a in fig. 5, and the average normal direction of the triangular patch of the observation area at the position a is the direction of arrow 1 (it can be seen that the position a is blocked by the tooth number 5 if observed from this direction).
Therefore, first, according to the angle between the average normal arrow 1 of the triangular patch of the observation area and the z-axis direction, whether the observation is performed from the top (z-axis direction in fig. 5) or from the side (x-and y-plane directions in fig. 5) is selected, and the top is not blocked. The method comprises the steps of obtaining 2 tooth positions (such as the tooth position No. 3 and the tooth position No. 4 in the figure 5) adjacent to the A position, calculating an included angle alpha formed by the average normal direction (arrow 1) of an observation area and the straight line direction (arrow 2) connected with the two tooth positions, indicating that shielding exists when the included angle alpha is smaller than a preset threshold value, forcibly changing an observation visual angle into the perpendicular bisector direction (such as the arrow 3 direction in the figure 5) of the two tooth positions, and accordingly avoiding the shielding problem of the observation visual angle.
Therefore, the non-shielded observation visual angle is calculated according to the lesion position, the target picture is automatically acquired and displayed, the observation visual angle of the target picture is associated with the observation visual angle of the three-dimensional tooth model, and the observation area of the specific lesion position is provided, so that an oral doctor can quickly position and count the lesion position, and omission is effectively avoided.
According to the oral cavity detection scheme based on artificial intelligence, network parameterization is carried out on a three-dimensional tooth model to obtain two-dimensional textures, and two-dimensional texture map samples corresponding to the three-dimensional tooth model samples are obtained; wherein, the two-dimensional texture map sample comprises a marked tooth area and a marked lesion area, the two-dimensional texture map sample is input into a target detection network for detection to obtain a training tooth area, the training tooth area is input into a semantic segmentation network for recognition to obtain a training lesion area, network parameters of the target detection network are adjusted based on a first comparison result of the training tooth area and the marked tooth area, and network parameters of the semantic segmentation network are adjusted based on a second comparison result of the training lesion area and the marked lesion area to obtain an oral cavity detection model, the two-dimensional texture map is detected based on the target detection network in the oral cavity detection model to obtain a plurality of tooth areas, each tooth area is recognized based on the semantic segmentation network in the oral cavity detection model to obtain a recognition result of each tooth area, and the lesion area is determined based on the recognition result of each tooth area, the method comprises the steps of obtaining two-dimensional coordinate points corresponding to a lesion area, obtaining three-dimensional coordinate points corresponding to the two-dimensional coordinate points based on a prestored dimension coordinate mapping relation table, determining lesion positions of a three-dimensional tooth model based on the three-dimensional coordinate points, obtaining an observation area based on the lesion positions, obtaining an average normal direction of the observation area, determining two adjacent tooth positions based on the lesion positions, determining a target straight line direction based on the two adjacent tooth positions, determining a target included angle based on the average normal direction and the target straight line direction, determining an observation visual angle based on the target included angle and a preset angle threshold value, and performing association processing on the observation visual angle and the visual angle of the three-dimensional tooth model. By adopting the technical scheme, the information of the whole set of teeth can be completely observed by detecting the two-dimensional texture map corresponding to the three-dimensional tooth model in the oral cavity detection process, the problem that a lesion area is missed due to improper selection of an observation visual angle of the three-dimensional tooth model is avoided, the tooth area is detected in the two-dimensional texture map, then the segmentation and identification of the lesion problem are carried out on the tooth area, the identification precision of small lesion areas such as decayed teeth and the like can be further improved, how to train the oral cavity detection model, and the observation visual angle of the screenshot is related to the observation visual angle of the three-dimensional tooth model, so that a stomacher can further conveniently and quickly position and count the lesion position, in addition, the lesion position can be presented in a three-dimensional model manner, the presentation effect is more visual, a target picture corresponding to a specific lesion position is finally provided, and the stomacher can conveniently and quickly position and count the lesion position, omission is effectively avoided, and detection efficiency and effect under the oral cavity detection scene are further improved.
Fig. 6 is a schematic structural diagram of an artificial intelligence-based oral cavity detection apparatus provided in an embodiment of the present disclosure, which may be implemented by software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 6, the apparatus includes:
an image obtaining module 301, configured to obtain a two-dimensional texture map corresponding to the three-dimensional tooth model;
a processing module 302, configured to process the two-dimensional texture map based on a pre-trained oral cavity detection model to obtain a lesion region;
a back projection module 303, configured to back-project the lesion area to the three-dimensional tooth model, so as to obtain a lesion position of the three-dimensional tooth model.
Optionally, the image obtaining module 301 is specifically configured to:
and carrying out network parameterization processing on the three-dimensional tooth model to obtain the two-dimensional texture map.
Optionally, the processing module 302 is specifically configured to:
detecting the two-dimensional texture map based on a target detection network in the oral cavity detection model to obtain a plurality of tooth areas;
identifying each tooth area based on a semantic segmentation network in the oral cavity detection model to obtain an identification result of each tooth area;
determining the lesion region based on the recognition result of each of the tooth regions.
Optionally, the back projection module 303 is specifically configured to:
acquiring a two-dimensional coordinate point corresponding to the lesion area;
acquiring a three-dimensional coordinate point corresponding to the two-dimensional coordinate point based on a prestored dimensional coordinate mapping relation table;
determining a lesion location of the three-dimensional tooth model based on the three-dimensional coordinate points.
Optionally, the apparatus further comprises:
a calculation module 304 for calculating an observation perspective based on the lesion location;
and an obtaining and displaying module 305, configured to obtain and display a target picture based on the observation angle.
Optionally, the calculating module 304 is specifically configured to:
acquiring an observation region based on the lesion position, and acquiring an average normal direction of the observation region;
determining two adjacent tooth positions based on the lesion position and determining a target straight line direction based on the two adjacent tooth positions;
determining a target included angle based on the average normal direction and the target straight line direction;
and determining the observation visual angle based on the target included angle and a preset angle threshold value.
Optionally, the apparatus further comprises:
the acquisition sample module is used for acquiring two-dimensional texture map samples corresponding to the three-dimensional tooth model samples; wherein the two-dimensional texture map sample comprises a marked tooth area and a marked lesion area;
the detection module is used for inputting the two-dimensional texture pattern into a target detection network for detection to obtain a training tooth area;
the recognition module is used for inputting the training tooth area into a semantic segmentation network for recognition to obtain a training lesion area;
and the training module is used for adjusting the network parameters of the target detection network based on the first comparison result of the training tooth area and the marked tooth area and adjusting the network parameters of the semantic segmentation network based on the second comparison result of the training lesion area and the marked lesion area to obtain the oral cavity detection model.
Optionally, the apparatus further includes an association module, configured to:
and carrying out association processing on the observation visual angle and the visual angle of the three-dimensional tooth model.
The oral cavity detection device based on artificial intelligence provided by the embodiment of the disclosure can execute the oral cavity detection method based on artificial intelligence provided by any embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method.
Embodiments of the present disclosure also provide a computer program product comprising a computer program/instructions that when executed by a processor implement the artificial intelligence based oral cavity detection method provided by any of the embodiments of the present disclosure.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Referring specifically to fig. 7, a schematic diagram of an electronic device 400 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 400 in the disclosed embodiment may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle mounted terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices, either wirelessly or by wire, to exchange data. While fig. 7 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or installed from the storage device 408, or installed from the ROM 402. The computer program, when executed by the processing device 401, performs the above-described functions defined in the artificial intelligence based oral cavity detection method of the embodiments of the present disclosure.
It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving information display triggering operation of a user in the video playing process; acquiring at least two pieces of target information related to the video; displaying first target information in the at least two pieces of target information in an information display area of a playing page of the video, wherein the size of the information display area is smaller than that of the playing page; and receiving a first switching trigger operation of a user, and switching the first target information displayed in the information display area into second target information of the at least two pieces of target information.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In accordance with one or more embodiments of the present disclosure, there is provided an electronic device including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement any of the artificial intelligence based oral detection methods provided by the present disclosure.
According to one or more embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for performing any of the artificial intelligence based oral detection methods provided by the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (11)
1. An oral cavity detection method based on artificial intelligence, which is characterized by comprising the following steps:
acquiring a two-dimensional texture map corresponding to the three-dimensional tooth model;
processing the two-dimensional texture map based on a pre-trained oral cavity detection model to obtain a lesion area;
and back projecting the lesion area to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model.
2. The artificial intelligence based oral cavity detection method according to claim 1, wherein the obtaining of the two-dimensional texture map corresponding to the three-dimensional tooth model comprises:
and carrying out network parameterization processing on the three-dimensional tooth model to obtain the two-dimensional texture map.
3. The artificial intelligence-based oral cavity detection method according to claim 1, wherein the detecting the two-dimensional texture map based on the pre-trained oral cavity detection model to obtain a lesion region comprises:
detecting the two-dimensional texture map based on a target detection network in the oral cavity detection model to obtain a plurality of tooth areas;
identifying each tooth area based on a semantic segmentation network in the oral cavity detection model to obtain an identification result of each tooth area;
determining the lesion region based on the recognition result of each of the tooth regions.
4. The artificial intelligence-based oral detection method of claim 1, wherein the back-projecting the lesion region to the three-dimensional tooth model to obtain a lesion location of the three-dimensional tooth model comprises:
acquiring a two-dimensional coordinate point corresponding to the lesion area;
acquiring a three-dimensional coordinate point corresponding to the two-dimensional coordinate point based on a prestored dimensional coordinate mapping relation table;
determining a lesion location of the three-dimensional tooth model based on the three-dimensional coordinate points.
5. The artificial intelligence-based oral detection method of claim 1, further comprising:
and calculating an observation visual angle based on the lesion position, and acquiring and displaying a target picture based on the observation visual angle.
6. The artificial intelligence based oral detection method of claim 5, wherein calculating an observation perspective based on the lesion location comprises:
obtaining an average normal of the lesion location;
determining two adjacent tooth positions based on the lesion position and determining a target straight line direction based on the two adjacent tooth positions;
determining a target included angle based on the average normal direction and the target straight line direction;
and determining the observation visual angle based on the target included angle and a preset angle threshold value.
7. The artificial intelligence-based oral cavity detection method according to claim 1, wherein before the detecting the two-dimensional texture map based on the pre-trained oral cavity detection model to obtain the lesion region, the method further comprises:
obtaining two-dimensional texture map samples corresponding to the three-dimensional tooth model samples; wherein the two-dimensional texture map sample comprises a marked tooth area and a marked lesion area;
inputting the two-dimensional texture pattern into a target detection network for detection to obtain a training tooth area;
inputting the training tooth area into a semantic segmentation network for recognition to obtain a training lesion area;
and adjusting network parameters of the target detection network based on a first comparison result of the training tooth area and the marked tooth area, and adjusting network parameters of the semantic segmentation network based on a second comparison result of the training lesion area and the marked lesion area to obtain the oral cavity detection model.
8. The artificial intelligence based oral detection method of claim 1, further comprising:
and performing association processing on the observation visual angle and the visual angle of the three-dimensional tooth model.
9. An oral cavity detection device based on artificial intelligence, comprising:
the image acquisition module is used for acquiring a two-dimensional texture map corresponding to the three-dimensional tooth model;
the processing module is used for processing the two-dimensional texture map based on a pre-trained oral detection model to obtain a lesion area;
and the back projection module is used for back projecting the lesion area to the three-dimensional tooth model to obtain the lesion position of the three-dimensional tooth model.
10. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor configured to read the executable instructions from the memory and execute the instructions to implement the artificial intelligence based oral detection method of any one of claims 1-8.
11. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the artificial intelligence based oral cavity detection method according to any one of the preceding claims 1-8.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210383604.3A CN114782345A (en) | 2022-04-12 | 2022-04-12 | Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium |
| PCT/CN2023/087798 WO2023198101A1 (en) | 2022-04-12 | 2023-04-12 | Artificial intelligence-based oral cavity examination method and apparatus, electronic device, and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210383604.3A CN114782345A (en) | 2022-04-12 | 2022-04-12 | Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114782345A true CN114782345A (en) | 2022-07-22 |
Family
ID=82428259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210383604.3A Pending CN114782345A (en) | 2022-04-12 | 2022-04-12 | Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114782345A (en) |
| WO (1) | WO2023198101A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023198101A1 (en) * | 2022-04-12 | 2023-10-19 | 先临三维科技股份有限公司 | Artificial intelligence-based oral cavity examination method and apparatus, electronic device, and medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7746339B2 (en) * | 2006-07-14 | 2010-06-29 | Align Technology, Inc. | System and method for automatic detection of dental features |
| CN111414809B (en) * | 2020-02-28 | 2024-03-05 | 上海牙典软件科技有限公司 | Three-dimensional pattern recognition method, device, equipment and storage medium |
| CN112515787B (en) * | 2020-11-05 | 2022-07-12 | 上海牙典软件科技有限公司 | Three-dimensional dental data analysis method |
| CN113262070A (en) * | 2021-05-18 | 2021-08-17 | 苏州苏穗绿梦生物技术有限公司 | Dental surgery equipment positioning method and system based on image recognition and storage medium |
| CN113425440A (en) * | 2021-06-24 | 2021-09-24 | 广州华视光学科技有限公司 | System and method for detecting caries and position thereof based on artificial intelligence |
| CN114782345A (en) * | 2022-04-12 | 2022-07-22 | 先临三维科技股份有限公司 | Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium |
-
2022
- 2022-04-12 CN CN202210383604.3A patent/CN114782345A/en active Pending
-
2023
- 2023-04-12 WO PCT/CN2023/087798 patent/WO2023198101A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023198101A1 (en) * | 2022-04-12 | 2023-10-19 | 先临三维科技股份有限公司 | Artificial intelligence-based oral cavity examination method and apparatus, electronic device, and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023198101A1 (en) | 2023-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111242881B (en) | Method, device, storage medium and electronic equipment for displaying special effects | |
| CN110766777A (en) | Virtual image generation method and device, electronic equipment and storage medium | |
| CN110062157B (en) | Method and device for rendering image, electronic equipment and computer readable storage medium | |
| CN109887077B (en) | Method and apparatus for generating three-dimensional model | |
| CN113570052B (en) | Image processing method, device, electronic equipment and storage medium | |
| CN108388889A (en) | Method and apparatus for analyzing facial image | |
| CN108510084A (en) | Method and apparatus for generating information | |
| CN114782345A (en) | Oral cavity detection method and device based on artificial intelligence, electronic equipment and medium | |
| CN115601811A (en) | Facial acne detection method and device | |
| CN111860388A (en) | Image processing method and device, electronic equipment and storage medium | |
| CN114842120A (en) | Image rendering processing method, device, equipment and medium | |
| US20220375258A1 (en) | Image processing method and apparatus, device and storage medium | |
| CN112308867B (en) | Tooth image processing method and device, electronic equipment and storage medium | |
| CN111862349A (en) | Virtual brush implementation method and device and computer readable storage medium | |
| CN113744384B (en) | Three-dimensional face reconstruction method and device, electronic equipment and storage medium | |
| CN111415308B (en) | Ultrasonic image processing method and communication terminal | |
| CN113110733A (en) | Virtual field interaction method and system based on remote duplex | |
| JP7253152B2 (en) | Information processing device, information processing method, and program | |
| CN109816791B (en) | Method and apparatus for generating information | |
| CN110047126B (en) | Method, apparatus, electronic device, and computer-readable storage medium for rendering image | |
| CN116245999A (en) | Text rendering method and device, electronic equipment and readable storage medium | |
| CN112652056B (en) | 3D information display method and device | |
| CN112634439B (en) | 3D information display method and device | |
| CN111292276B (en) | Image processing method and device | |
| CN116527993A (en) | Video processing method, apparatus, electronic device, storage medium and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |