CN114638801A - Upper airway ventilation condition analysis method and device and storage medium - Google Patents
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Abstract
The invention discloses an upper airway ventilation condition analysis method, an upper airway ventilation condition analysis device and a storage medium, and relates to the field of upper airway analysis, wherein the upper airway ventilation condition analysis method comprises the steps of S1 establishing an initial key point positioning model and an initial key point accurate positioning model, and S processing an original image sample set to obtain a first training sample set; s3, importing a first training sample set into initial key point positioning model training; s4 intercepting the first training sample set as a second training sample set; s5, importing a second training sample set into an initial key point accurate positioning model for training; s6, importing the upper airway image to be analyzed into a key point positioning model to obtain position data; s7 intercepting the key point position area and importing the key point position area into an optimized key point accurate positioning model to obtain accurate position data; s8, measuring key index data; s9 analyzing ventilation of the upper airway; by adopting the deep learning technology, the automatic measurement and analysis of the upper airway ventilation obstacle condition of the person to be analyzed are realized, and the clinical work efficiency and the accuracy of the analysis and judgment of the upper airway ventilation obstacle condition of the person to be analyzed are improved.
Description
Technical Field
The invention relates to the field of upper airway analysis, in particular to an upper airway ventilation condition analysis method, an upper airway ventilation condition analysis device and a storage medium.
Background
The upper airway ventilation disorder of the child has great influence on the whole body health of the child, so that the diagnosis can be made timely and accurately, and a doctor is assisted to make a reasonable treatment plan. The influence on the oral cavity is mainly shown in oral cavity symptoms such as mouth breathing, palatal vault arch, upper front labial inclination, upper labial eversion, and retrodentition, which influence the beauty of the face and the occlusion of teeth of a patient and need to be corrected. In the tooth correction of the patients, the upper airway ventilation obstacle condition of the patients needs to be analyzed and judged, and in general, orthodontists realize the analysis and judgment of the upper airway ventilation obstacle condition of the patients through the measurement and analysis of the craniofacial side X-ray film of the patients. In the analysis and judgment process, key anatomical landmark points related to the upper airway and the skull side X-ray film in the patient need to be marked, relevant key index data are measured and calculated according to the key points, and the data are analyzed to obtain the ventilation obstacle condition of the upper airway of the patient.
The traditional clinical measurement and calculation of key anatomical landmark point marks and key index data mainly depends on visual observation and hand drawing of an orthodontist, so that the problems of low efficiency and low precision exist.
Disclosure of Invention
The invention aims to solve the problems and designs an upper airway ventilation condition analysis method, an upper airway ventilation condition analysis device and a storage medium.
The invention realizes the purpose through the following technical scheme:
an upper airway ventilation condition analysis method, comprising:
s1, under a pyrrch-based framework, constructing a neural network by using a convolution network (CNN) and full connection combined mode to establish an initial key point positioning model and an initial key point accurate positioning model, wherein the width and the depth of the neural network of the initial key point accurate positioning model are respectively smaller than the width and the depth of the neural network of the initial key point positioning model;
s2, acquiring and processing an original image sample set to obtain a first training sample set;
s3, importing the first training sample set into an initial key point positioning model, and performing training optimization on the initial key point positioning model to obtain an optimized key point positioning model;
s4, intercepting the area of the key points in the first training sample set as a second training sample set;
s5, importing the second training sample set into the initial key point accurate positioning model, and performing training optimization on the initial key point accurate positioning model to obtain an optimized key point accurate positioning model;
s6, importing the upper airway image to be analyzed into an optimization key point positioning model to obtain position data of the upper airway key point to be analyzed;
s7, intercepting the area of the key point position of the upper airway image to be analyzed, and leading the area into an optimized key point accurate positioning model to obtain accurate position data of the key point of the upper airway to be analyzed;
s8, measuring and calculating key index data of the upper airway image to be analyzed;
and S9, analyzing the key index data to obtain the ventilation condition of the upper airway.
The upper airway ventilation condition analysis device comprises a storage and a processor, wherein the storage stores a program, and the program is executed by the processor to execute the upper airway ventilation condition analysis method.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the upper airway ventilation analysis method as set forth above.
The invention has the beneficial effects that: by adopting the deep learning technology, the automatic measurement and analysis of the upper airway ventilation obstacle condition of the person to be analyzed are realized, the clinical work efficiency and the upper airway ventilation obstacle condition analysis and judgment accuracy of the person to be analyzed are improved, the human resources are greatly saved, and the influence of human factors is also avoided.
Drawings
FIG. 1 is a schematic flow chart of the upper airway ventilation analysis method of the present invention;
FIG. 2 is a schematic diagram of upper airway key points;
FIG. 3 is a schematic diagram of upper airway key indicator data measurement calculation;
wherein corresponding reference numerals are:
v is an epiglottis valley point, U is a uvula tip point, PNS is a posterior nasal acanthosis point, Go is a mandibular angle point, B is a lower alveolar socket point, Ba is a skull base point, and S is a sphenoidale point;
LPW is a drop foot point which is perpendicular to the posterior pharyngeal wall after crossing the epiglottis valley point, TB is an intersection point of a connecting line of a mandibular angle point-lower alveolar socket point and the root of the tongue, TPPW is an intersection point of an extension line of the connecting line of the mandibular angle point-lower alveolar socket point and the posterior pharyngeal wall, MPW is a drop foot which is perpendicular to the posterior pharyngeal wall after crossing the uvula point, UPW is an intersection point of a connecting line of a posterior nasal acanthosis point-skull base point and the posterior pharyngeal wall, and AD2 is an intersection point of a connecting line from a posterior nasal acanthosis point to the upper midpoint of a connecting line of a sphenoid sadum point and the skull base point and the posterior pharyngeal wall.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "inside", "outside", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, or the orientations or positional relationships that the products of the present invention are conventionally placed in use, or the orientations or positional relationships that are conventionally understood by those skilled in the art, and are used for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like are used solely to distinguish one from another, and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly stated or limited, the terms "disposed" and "connected" are to be interpreted broadly, and for example, "connected" may be a fixed connection, a detachable connection, or an integral connection; can be mechanically or electrically connected; the connection may be direct or indirect through an intermediate medium, and the connection may be internal to the two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The following detailed description of embodiments of the invention refers to the accompanying drawings.
An upper airway ventilation condition analysis method, comprising:
s1, under the framework of the pyrrch, constructing a neural network by using a convolution network (CNN) and full connection combined mode to establish an initial key point positioning model and an initial key point accurate positioning model, wherein the width and the depth of the neural network of the initial key point accurate positioning model are respectively smaller than the width and the depth of the neural network of the initial key point positioning model.
S2, acquiring and processing an original image sample set to obtain a first training sample set, wherein the original image sample set is a plurality of skull side X-ray film image samples, and the processing of the original image sample set specifically comprises the following steps: and labeling the upper airway key points of each image sample to obtain a first training sample set, and dividing the first training sample set into a first training set, a first verification set and a first test set.
S3, importing the first training sample set into an initial key point positioning model, and performing training optimization on the initial key point positioning model to obtain an optimized key point positioning model; the method specifically comprises the following steps:
s31, importing an initial key point positioning model by a first training set;
s32, training and optimizing the key point positioning model by the image samples of the first training set to obtain an optimized key point positioning model;
s33, judging whether the optimized key point positioning model meets the optimization condition, if so, taking the optimized key point positioning model as a preliminary key point positioning model, and entering S34; otherwise, returning to S32; s34, the first test set and the first verification set carry out test verification on the preliminary key point positioning model, if the test verification is passed, the preliminary key point positioning model is used as an optimized key point positioning model, and the process goes to S4; if not, the first training set sample is incremented and the process returns to S31.
S4, intercepting the area of the key points in the first training sample set as a second training sample set, wherein the size of the area is as follows: the key point is taken as the center, the length of the area is half of the distance between Ba and S, the width of the area is a rectangle of half of the distance between Ba and S, and Ba and S are two key points on the X sheet.
S5, importing the second training sample set into the initial key point accurate positioning model, and performing training optimization on the initial key point accurate positioning model to obtain an optimized key point accurate positioning model; the method specifically comprises the following steps:
s51, importing an initial key point accurate positioning model by a second training set;
s52, training and optimizing the accurate key point positioning model by the image samples of the second training set to obtain an optimized accurate key point positioning model;
s53, judging whether the optimized key point accurate positioning model meets the optimization condition, if so, taking the optimized key point accurate positioning model as a primary key point accurate positioning model, and entering S54; otherwise, returning to S52;
s54, the second test set and the second verification set carry out test verification on the preliminary key point positioning model, if the test verification is passed, the preliminary key point accurate positioning model is used as an optimized key point accurate positioning model, and the process enters S6; if not, the second training set sample is incremented and the process returns to S51.
S6, importing the upper airway image to be analyzed into an optimization key point positioning model to obtain position data of the upper airway key point to be analyzed;
s7, intercepting the key point position area of the upper airway image to be analyzed, and leading the key point position area into an optimized key point accurate positioning model to obtain accurate position data of the upper airway key point to be analyzed;
s8, measuring and calculating key index data of the upper airway image to be analyzed, wherein the key index data comprise a shortest distance value from an epiglottis valley point to a pharyngeal back wall, a distance value from a TB point to a TPPW point, a shortest distance value from a uvula point to a pharyngeal back wall, a distance value from a retronasal spinous point to a UPW point and a distance value from the retronasal spinous point to an AD2 point, the TB point is an intersection point of a connecting line of a mandibular angle point-mandibular socket point and a tongue root, the TPPW point is an intersection point of an extension line of the mandibular angle point-mandibular socket point connecting line and the pharyngeal back wall, and AD2 is an intersection point of a connecting line of the retronasal spinous point to a middle point of a sphenoid saddle point and a skull base point and the pharyngeal back wall.
And S9, comparing the key index data obtained by measurement and calculation with the key index data of the standard upper airway to obtain the ventilation condition of the upper airway.
In S33 and S53, the optimization condition is whether the number of iterations reaches a predetermined value or not, and the loss error of the model reaches a predetermined expected value or not, and the loss function mselos is the mean square error between the output of the neural network and the euclidean distance of the label, and is expressed as
The upper airway ventilation condition analysis device comprises a storage and a processor, wherein the storage stores a program, and the program is executed by the processor to execute the upper airway ventilation condition analysis method.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the upper airway ventilation analysis method as set forth above.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (10)
1. An upper airway ventilation condition analysis method, comprising:
s1, under a pyrrch-based framework, constructing a neural network by using a convolution network (CNN) and full connection combined mode to establish an initial key point positioning model and an initial key point accurate positioning model, wherein the width and the depth of the neural network of the initial key point accurate positioning model are respectively smaller than the width and the depth of the neural network of the initial key point positioning model;
s2, acquiring and processing an original image sample set to obtain a first training sample set;
s3, importing the first training sample set into an initial key point positioning model, and carrying out training optimization on the initial key point positioning model to obtain an optimized key point positioning model;
s4, intercepting the area of the key points in the first training sample set as a second training sample set;
s5, importing the second training sample set into the initial key point accurate positioning model, and performing training optimization on the initial key point accurate positioning model to obtain an optimized key point accurate positioning model;
s6, importing the upper airway image to be analyzed into an optimization key point positioning model to obtain position data of the upper airway key point to be analyzed;
s7, intercepting the key point position area of the upper airway image to be analyzed, and leading the key point position area into an optimized key point accurate positioning model to obtain accurate position data of the upper airway key point to be analyzed;
s8, measuring and calculating key index data of the upper airway image to be analyzed;
and S9, analyzing the key index data to obtain the ventilation condition of the upper airway.
2. The upper airway ventilation analysis method according to claim 1, wherein in S2, the original image sample set is a plurality of lateral cranial X-ray image samples, the upper airway key points of each image sample are labeled to obtain a first training sample set, and the first training sample set is divided into a first training set, a first verification set and a first test set.
3. The upper airway ventilation analysis method of claim 2, comprising in S3:
s31, importing an initial key point positioning model by a first training set;
s32, training and optimizing the key point positioning model by the image samples of the first training set to obtain an optimized key point positioning model;
s33, judging whether the optimized key point positioning model meets the optimization condition, if so, taking the optimized key point positioning model as a preliminary key point positioning model, and entering S34; otherwise, returning to S32;
s34, the first test set and the first verification set carry out test verification on the preliminary key point positioning model, if the test verification is passed, the preliminary key point positioning model is used as an optimized key point positioning model, and the process goes to S4; if not, the first training set sample is incremented and the process returns to S31.
4. The upper airway ventilation analysis method of claim 1, comprising in S5:
s51, importing an initial key point accurate positioning model by a second training set;
s52, training and optimizing the accurate key point positioning model by the image samples of the second training set to obtain an optimized accurate key point positioning model;
s53, judging whether the optimized key point accurate positioning model meets the optimization condition, if so, taking the optimized key point accurate positioning model as a primary key point accurate positioning model, and entering S54; otherwise, returning to S52;
s54, the second test set and the second verification set carry out test verification on the preliminary key point positioning model, if the test verification is passed, the preliminary key point accurate positioning model is used as an optimized key point accurate positioning model, and the process enters S6; if not, the second training set sample is incremented and the process returns to S51.
5. The upper airway ventilation analysis method according to claim 3, wherein in S33, the optimization condition is whether any one of a preset number of iterations is reached and a loss error of the optimized keypoint location model reaches a preset expected value.
6. The upper airway ventilation analysis method according to claim 4, wherein in S53, the optimization condition is whether any one of a preset number of iterations is reached and whether the loss error of the optimized keypoint accurate positioning model reaches a preset expected value.
7. The upper airway ventilation condition analysis method according to claim 1, wherein in S8, the key index data includes a shortest distance value from an epiglottis valley point to a posterior pharyngeal wall, a distance value from a TB point to a TPPW point, a shortest distance value from a uvula cusp point to a posterior pharyngeal wall, and a distance value from a posterior nasal spinous point to a UPW point, wherein the TB point is an intersection point of a connection line between a mandibular angle point and a mandibular alveolar socket point and a tongue root, and the TPPW point is an intersection point of an extension line of the connection line between the mandibular angle point and the mandibular alveolar socket point and the posterior pharyngeal wall.
8. The upper airway ventilation analysis method of claim 1, wherein in S9, the measured and calculated key indicator data is compared with key indicator data of a standard upper airway to obtain the ventilation of the upper airway.
9. Upper airway ventilation analysis apparatus comprising a memory and a processor, the memory storing a program which, when executed by the processor, performs the upper airway ventilation analysis method of any of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for upper airway ventilation analysis according to any one of claims 1 to 8.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599830A (en) * | 2016-12-09 | 2017-04-26 | 中国科学院自动化研究所 | Method and apparatus for positioning face key points |
| CN108764048A (en) * | 2018-04-28 | 2018-11-06 | 中国科学院自动化研究所 | Face critical point detection method and device |
| CN109886121A (en) * | 2019-01-23 | 2019-06-14 | 浙江大学 | A kind of face key independent positioning method blocking robust |
| CN113705444A (en) * | 2021-08-27 | 2021-11-26 | 成都玻尔兹曼智贝科技有限公司 | Facial development analysis and evaluation method and system |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599830A (en) * | 2016-12-09 | 2017-04-26 | 中国科学院自动化研究所 | Method and apparatus for positioning face key points |
| CN108764048A (en) * | 2018-04-28 | 2018-11-06 | 中国科学院自动化研究所 | Face critical point detection method and device |
| CN109886121A (en) * | 2019-01-23 | 2019-06-14 | 浙江大学 | A kind of face key independent positioning method blocking robust |
| CN113705444A (en) * | 2021-08-27 | 2021-11-26 | 成都玻尔兹曼智贝科技有限公司 | Facial development analysis and evaluation method and system |
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