CN111938655A - Orbit soft tissue form evaluation method, system and equipment based on key point information - Google Patents
Orbit soft tissue form evaluation method, system and equipment based on key point information Download PDFInfo
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Abstract
The invention provides a method, a system and equipment for evaluating the form of soft tissues of an eye socket based on key point information, wherein an LSFM (local surface modulation) model is used for registering three-dimensional face data, and the three-dimensional face data is calibrated and normalized on the basis of a coordinate space where a standard movable model is located; recording indexes of the feature points of the left eye region and the right eye region and the number of the feature points, and extracting the feature points of the left eye region and the right eye region; judging corresponding left and right eye characteristic point regions before and after the operation from the three-dimensional face data by adopting a linear regression model to obtain an average displacement matrix and whether the operation is performed in the corresponding region, obtaining independent variables and dependent variables of linear regression, and calculating a weight of the influence of the direction on the result; and calculating an average displacement matrix of the operative eye region and the standard movable model and an average displacement matrix of the non-operative eye region and the standard movable model, and setting a threshold value to judge the recovery condition of the soft tissues of the postoperative orbit. The accuracy of the invention reaches 90%.
Description
Technical Field
The invention relates to the field of three-dimensional image processing, in particular to an orbit soft tissue morphology evaluation method, system and device based on key point three-dimensional information.
Background
Diagnosis of orbit diseases, decision of treatment schemes, evaluation of curative effect and the like mainly depend on CT and MRI data of patients at present. In recent years, with the increasing aesthetic needs, medical decision making and patient-ported output based on subjective feelings of patients are more and more emphasized. Therefore, on the basis of the pre-determination of imaging science, the determination based on the facial measurement is gradually becoming a new factor for the diagnosis and treatment decision of the orbital disease of the image. Orbital diseases comprise vascular diseases, tumor diseases, trauma, inflammation and the like, and many diseases such as thyroid associated eye disease (TAO), orbital fracture and the like can have multiple, rapid and transient clinical manifestation changes in different periods of the disease process, particularly the changes in soft tissue morphology. Although the changes can be captured by means of imaging examination, the cost of CT examination is the radiation of rays, and the limiting factors of MRI are cost and examination time, so that the two types of examination are only used in diagnosis decision and important follow-up nodes, are not items which can be repeatedly detected for many times, at any time and repeatedly, cannot provide reliable data for rapidly changing physical signs of patients, and limit the promotion space of an orbit doctor in the aspects of cognition and intervention in the aspects of early prompt of high-risk changes of the patients, rapid response, rehabilitation guidance and the like.
Orbital fracture is a kind of orbit disease with large specific gravity, and is mostly caused by trauma, resulting in deformity of the middle of face, dyskinesia of eyeball, double vision, visual deterioration and even blindness. TAO causes clinical symptoms such as exophthalmos, eyelid recession, and dysoculomosia, and irreversible damage such as corneal perforation and optic nerve injury occurs in severe patients, and the quality of life is seriously reduced. In addition to considerations of bony structure and visual function, changes in appearance are also increasingly important factors in surgical decision making, whether orbital fractures or TAOs. The perceivable orbital facial morphology change, the orbital swelling degree, the regression of the orbital soft tissue swelling, etc. are all patient-related outchomes or complaints which are of considerable importance in the aspects of patient treatment purpose and self-evaluation of the treatment effect, etc. However, as previously mentioned, we have no better index than subjective "presence or absence of judgment" other than CT and MRI and indices such as the degree of eyeball protrusion based on or not based on these imaging examinations. In the case of orbital fracture patients, patients who are eligible for surgical indications often have or do not have all or some of the following characteristics over the course of the disease: the change of the orbital facial morphology can be detected before the affected side operation, the soft tissue of the orbit is swollen immediately after the operation, and the short-term or long-term swelling is subsided after the operation. The evaluation of the content in the past only depends on the subjective feelings of the doctor and the patient, and the quantitative evaluation with repeatability cannot be carried out, so that the evaluation cannot be used as a reliable observation index, and the participation degree of the visual angle of the patient in the disease diagnosis and treatment process is also seriously reduced.
At present, no explanation or report of the similar technology of the invention is found, and similar data at home and abroad are not collected.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an orbit soft tissue form evaluation method, system and equipment based on key point three-dimensional information.
The invention is realized by the following technical scheme.
According to one aspect of the invention, a method for evaluating the morphology of soft tissues in an orbit based on key point information is provided, which comprises the following steps:
registering the three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located;
recording indexes of feature points of left and right eye regions and the number n of the feature points, and extracting the feature points of the left and right eye regions from the three-dimensional face data as key points;
judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterTo obtain the ith average displacement matrix PiAnd whether the corresponding area is operated YiThen obtaining independent variable P and dependent variable Y of linear regression, and calculating weight W of the influence of x, Y and z directions on the result;
calculating the average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalAnd setting a threshold value thread to judge the postoperative recovery condition of the soft tissues of the eye socket.
Preferably, the registration adopts a non-rigid iterative closest point algorithm to correspond the points in the three-dimensional face data to the LSFM standard movable model.
Preferably, the calibration and normalization refers to unifying the spatial position and the data size of the three-dimensional face data.
Preferably, the feature point index is to select feature points in accordance with the left and right eye surgery areas from texture data of the standard movable model and record numbers of the feature points.
Preferably, the number n of feature points includes 2572 feature points of the left-eye region and/or 2591 feature points of the right-eye region.
Preferably, the average displacement matrix Pi＝∑(Pi_after-Pi_before)/n。
Preferably, said correspondence isSurgical condition of the region
Preferably, the independent variable
Wherein m is the number of training set samples.
Preferably, said dependent variable
Wherein m is the number of training set samples.
Preferably, the weight W ═ P (P)T·P)-1·PT·Y。
Preferably, the threshold thread is 0.006.
Preferably, the judging of the postoperative soft tissue recovery condition of the orbit is as follows:
when P is presentoperation-thread<Pnatural<PoperationWhen the current value is + thread, the recovery is judged to be good, otherwise, the recovery is judged not to be recovered.
According to another aspect of the present invention, there is provided an orbit soft tissue morphology evaluation system based on three-dimensional information of key points, comprising:
a preprocessing module: registering the three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located;
a feature extraction module: recording indexes of feature points of left and right eye regions and the number n of the feature points, and extracting the feature points of the left and right eye regions from the three-dimensional face data;
an operation area judgment module: judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterObtaining the ith average displacement matrix PiAnd whether the corresponding area is operated YiThen obtaining the independent variable P and the dependent variable Y of the linear regression, and calculating x,The weight W of the effect of the y and z directions on the result;
postoperative resumes judging module: calculating the average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalAnd setting a threshold value thread to judge the postoperative recovery condition of the soft tissues of the eye socket.
According to a third aspect of the present invention, there is provided an apparatus comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor being operable when executing the computer program to perform any of the methods described above.
According to a fourth aspect of the present invention, there is provided another apparatus comprising a memory, a processor, and the system provided above stored on the memory and executable by the processor.
Compared with the prior art, the invention has at least one of the following beneficial effects:
according to the orbit soft tissue morphology evaluation method and system based on the key point three-dimensional information, provided by the invention, because the method and the system are based on image discrimination with the traditional image, direct intervention is not involved, and the risk is not increased. Besides meeting the basic principle requirement of replacing the endpoint in the aspect of clinical test, the method and the system have the advantages of convenience in index acquisition, repeatability in the measurement process and quantification in the evaluation mode.
According to the method and the system for evaluating the form of the soft tissues of the eye socket based on the three-dimensional information of the key points, provided by the invention, the medical diagnosis part is automated and repeatable, and the accuracy rate reaches 90%, according to the characteristic that the shape of the model is not changed and only the topological structure is changed in the registration process of the three-dimensional model of the face.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic diagram illustrating results of registering, calibrating and normalizing three-dimensional face data in the method for evaluating the form of soft tissues of an orbit based on three-dimensional information of key points according to the embodiment of the present invention; wherein: (a) the model is an original model, (b) is a registered model, and (c) is a calibrated and normalized model;
fig. 2 is a schematic diagram of left and right eye regions extracted in the orbit soft tissue morphology evaluation method based on the three-dimensional information of the key points according to the embodiment of the invention; wherein: fig. a is a left eye region model, and fig. b is a right eye region model;
fig. 3 is a flowchart of an orbit soft tissue morphology evaluation method based on three-dimensional key point information according to an embodiment of the present invention.
Detailed Description
The following examples illustrate the invention in detail: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
An embodiment of the invention provides an orbit soft tissue morphology evaluation method based on key point information, the method can be used for automatically judging the orbit soft tissue swelling condition of a postoperative patient, and the method and the system can be used for automatically judging the orbit soft tissue swelling condition of the postoperative patient. As shown in fig. 3, the method includes:
the method comprises the steps of firstly, registering three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located.
As a preferred embodiment, the registration adopts a non-rigid iterative closest point algorithm to correspond the points in the three-dimensional face data to the LSFM standard movable model.
As a preferred embodiment, the calibration and normalization refers to unifying the spatial position and the data size of the three-dimensional face data.
The specific registration, calibration and normalization results in the first step are shown in fig. 1.
And secondly, recording indexes of the feature points of the left eye region and the right eye region and the number n of the feature points, and extracting the feature points (namely key points) of the left eye region and the right eye region from the three-dimensional face data.
As a preferred embodiment, the feature point index is to select feature points in the texture data of the standard movable model, which are in accordance with the surgical area of the left and right eyes, and record the numbers of the feature points.
As a preferred embodiment, the number of feature points includes 2572 feature points for the left eye region and 2591 feature points for the right eye region.
Thirdly, judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterTo obtain the ith average displacement matrix PiAnd whether the corresponding area is operated YiAnd then obtaining an independent variable P and a dependent variable Y of the linear regression, and calculating a weight W of the influence of the directions x, Y and z on the result.
As a preferred embodiment, the average displacement matrix Pi＝∑(Pi_after-Pi_before)/n。
As a preferred embodiment, the surgical condition of the corresponding region
As a preferred embodiment, the independent variable
Wherein m is the number of training set samples.
As a preferred embodiment, dependent variables
Wherein m is the number of training set samples.
As a preferred embodiment, the weight W ═ P (P)T·P)-1·PT·Y。
In the third step, W ═ 47.85693945, 2.64343121, 79.23895416, -0.37536196, four values correspond to the weight of the model x coordinate on the result, the weight of the y coordinate on the result, the weight of the z coordinate on the result, and the error term, respectively.
Fourthly, calculating an average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalAnd setting a threshold value thread to judge the postoperative recovery condition of the soft tissues of the eye socket.
As a preferred embodiment, the threshold thread is 0.006;
as a preferred embodiment, the postoperative soft tissue recovery condition of the orbit is judged as follows:
when P is presentoperation-thread<Pnatural<PoperationWhen the current value is + thread, the recovery is judged to be good, otherwise, the recovery is judged not to be recovered.
In the fourth step Poperation＝0.01588，PnaturalWhen the determination result is not restored at 0.00865, the result is consistent with the actual situation, as shown in fig. 1 and 2.
Effects of the implementation
According to the above steps, three-dimensional face models of 65 patients were diagnosed, of which 59 achieved correct diagnosis.
Another embodiment of the present invention provides an orbit soft tissue morphology evaluation system based on keypoint information, including:
a preprocessing module: registering the three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located;
a feature extraction module: recording indexes of feature points of left and right eye regions and the number n of the feature points, and extracting the feature points of the left and right eye regions from the three-dimensional face data;
an operation area judgment module: judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterObtaining the ith average displacement matrix PiAnd whether the corresponding area is operated YiThen obtaining independent variable P and dependent variable Y of linear regression, calculating the result shadow in x, Y and z directionsThe sound weight W;
postoperative resumes judging module: calculating the average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalAnd setting a threshold value thread to judge the postoperative recovery condition of the soft tissues of the eye socket.
A third embodiment of the present invention provides an apparatus, which includes a memory, a processor, and a computer program stored in the memory and capable of being executed on the processor, wherein the processor, when executing the computer program, can be configured to perform a method for morphological evaluation of soft tissues of an orbit based on keypoint information.
Optionally, a memory for storing a program; a Memory, which may include a volatile Memory (RAM), such as a Random Access Memory (SRAM), a Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), and the like; the memory may also comprise a non-volatile memory, such as a flash memory. The memory 62 is used to store computer programs (e.g., applications, functional modules, etc. that implement the above-described methods), computer instructions, etc., which may be stored in one or more memories in a partitioned manner. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
The computer programs, computer instructions, etc. described above may be stored in one or more memories in a partitioned manner. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
A processor for executing the computer program stored in the memory to implement the steps of the method according to the above embodiments. Reference may be made in particular to the description relating to the preceding method embodiment.
The processor and the memory may be separate structures or may be an integrated structure integrated together. When the processor and the memory are separate structures, the memory, the processor may be coupled by a bus.
A fourth embodiment of the invention provides another apparatus comprising a memory, a processor, and a keypoint information-based orbital soft tissue morphology evaluation system stored on the memory and executable by the processor.
The same or similar memory and processor as described above may be used in this embodiment and will not be described here.
The orbit soft tissue morphology evaluation method, system and device based on the key point information (three-dimensional information) provided by the embodiment of the invention are a new orbit soft tissue morphology evaluation technology which is rapid, quantitative and good in repeatability, and the orbit soft tissue quantitative evaluation based on the SKO provides rapid, quantitative and repeatable evaluation for the eye soft tissue morphology description. Extracting a plurality of mesh vertexes of an orbit region 2500 from three-dimensional information of a Face of a patient with an orbit disease acquired by a Bellus3D structured light scanner to establish an orbit soft tissue evaluation key point set (SKO, soft-tissue key-points-set of the orbit), and registering key points by using an LSFM (Large Scale Face model); an SKO regression equation is established by using 65 orbital fracture patient data, and the form of orbital soft tissues is judged, so that objective evaluation of subjective judgments such as the change of the form of the perceivable orbital face, the swelling and regression of the orbital is realized, effective verification is obtained, and a new thought is provided for diagnosis and treatment and decision research of orbital face diseases. Compared with the prior art, the method reduces the requirement on manpower, basically realizes the automation of medical diagnosis, has the accuracy rate of 90 percent, and basically meets the clinical requirement.
It should be noted that, the steps in the method provided by the present invention can be implemented by using corresponding modules, devices, units, and the like in the system, and those skilled in the art can implement the step flow of the method by referring to the technical scheme of the system, that is, the embodiment in the system can be understood as a preferred example of the implementation method, and details are not described herein.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices provided by the present invention in purely computer readable program code means, the method steps can be fully programmed to implement the same functions by implementing the system and its various devices in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices thereof provided by the present invention can be regarded as a hardware component, and the devices included in the system and various devices thereof for realizing various functions can also be regarded as structures in the hardware component; means for performing the functions may also be regarded as structures within both software modules and hardware components for performing the methods.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (10)
1. A method for evaluating the morphology of soft tissues of an orbit based on key point information is characterized by comprising the following steps:
registering the three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located;
recording indexes of feature points of left and right eye regions and the number n of the feature points, and extracting the feature points of the left and right eye regions from the three-dimensional face data as key points;
judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterTo obtain the ith average displacement matrix PiAnd whether the corresponding area is operated YiThen obtaining independent variable P and dependent variable Y of linear regression, and calculating weight W of the influence of x, Y and z directions on the result;
calculating the average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalSetting a threshold value of the thread to judge the postoperative eyeRecovery of soft orbital tissue.
2. The method for evaluating the morphology of soft tissues of the orbit based on the key point information according to claim 1, characterized in that the registration adopts a non-rigid iterative closest point algorithm to correspond the points in the three-dimensional face data to the standard movable model.
3. The method for evaluating the morphology of soft tissues of the eye socket based on the keypoint information according to claim 1, wherein the calibration and normalization are performed by unifying the spatial position and the data size of the three-dimensional face data.
4. The method for evaluating morphology of soft tissues of eye sockets based on keypoint information according to claim 1, wherein the feature point index is to select feature points in the texture data of a standard movable model, which conform to the surgical area of the left and right eyes, and record the numbers of the feature points.
5. The method for evaluating morphology of soft tissues of eye socket based on keypoint information according to claim 1, wherein the number n of feature points comprises 2572 feature points of a left eye region and/or 2591 feature points of a right eye region.
6. The method for evaluating soft tissue morphology of eye socket based on keypoint information according to claim 1, characterized in that said average displacement matrix Pi＝∑(Pi_after-Pi_before)/n；
Surgical condition of the corresponding area
The independent variable
Wherein m is the number of training set samples;
dependent variable of the
Wherein m is the number of training set samples;
the weight W ═ PT·P)-1·PT·Y。
7. The method for evaluating morphology of soft tissues of eye orbit based on keypoint information according to claim 1, wherein the threshold value thread is 0.006;
the judgment of the postoperative soft tissue recovery condition of the eye socket is as follows:
when P is presentoperation-thread<Pnatural<PoperationWhen the current value is + thread, the recovery is judged to be good, otherwise, the recovery is judged not to be recovered.
8. A system for evaluating the morphology of soft tissues in an orbit based on keypoint information is characterized by comprising:
a preprocessing module: registering the three-dimensional face data by using an LSFM model, and calibrating and normalizing the registered three-dimensional face data on the basis of a coordinate space where a standard movable model is located;
a feature extraction module: recording indexes of feature points of left and right eye regions and the number n of the feature points, and extracting the feature points of the left and right eye regions from the three-dimensional face data;
an operation area judgment module: judging whether the operation area is positioned on the left eye or the right eye by adopting a linear regression model, wherein the operation area comprises a left eye characteristic point area P and a right eye characteristic point area P which correspond to the ith three-dimensional face data before and after the operationi_before,Pi_afterObtaining the ith average displacement matrix PiAnd whether the corresponding area is operated YiThen obtaining independent variable P and dependent variable Y of linear regression, and calculating weight W of the influence of x, Y and z directions on the result;
postoperative resumes judging module: calculating the average displacement matrix P of the operation eye area and the standard movable modeloperationAnd the average displacement matrix P of the non-operative ocular region and the standard mobile modelnaturalSetting threshold value thread to judge postoperative orbital soft tissue recoveryThe situation is.
9. An apparatus comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the computer program, when executed by the processor, is operable to perform the method of any of claims 1 to 7.
10. An apparatus comprising a memory, a processor, and the system provided in claim 8 stored on the memory and executable by the processor.
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