CN111210427B - Time-change-based method for analyzing post-operation shrinkage of in-vivo light-weight patch - Google Patents
Time-change-based method for analyzing post-operation shrinkage of in-vivo light-weight patch Download PDFInfo
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Abstract
The invention discloses a method for analyzing post-operation shrinkage of an in-vivo light-weight patch based on time change, which comprises the following steps: respectively preprocessing the cross-section sequence and the sagittal plane sequence of the ABUS images at two time points to respectively separate the patch sequences of the cross section and the sagittal plane; constructing and smoothing three-dimensional models of patch sequences in two directions of a cross section and a sagittal plane respectively; fusing the two three-dimensional models of the cross section and the sagittal plane; wherein the two time points correspond to a state in which the patch has not been shrunken and a state in which the patch has been shrunken, respectively. And correspondingly selecting the three-dimensional model with the minimum error rate with the real patch from the three-dimensional models at two time points, and comparing the spatial variation difference between the two selected three-dimensional models to realize the analysis of post-operation shrinkage of the patch. The method has high accuracy, can effectively solve the problem of difficult non-invasive detection of the lightweight patch, and can objectively describe and quantify the shrinkage rate and the spatial variation condition of the patch implanted in the body of a patient.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an in-vivo light-weight patch post-operation shrinkage analysis method based on time change.
Background
The light patch is soft and comfortable, has moderate price, small foreign body residual quantity and low risk of rejection infection. Traditional incisional hernia high mass patches (expanded polytetrafluoroethylene patches, weight-based patches, and conforming patches) are gradually being replaced by lightweight patches based on the trend toward patch development with minimal foreign body residue and surgical cost control considerations. While conventional three-dimensional imaging techniques, such as CT and MRI, are capable of visualizing certain large mass patches, they are not capable of visualizing thin, lightweight patches that are comfortable and can significantly reduce inflammatory responses. While two-dimensional hand-held ultrasound (HHUS) is capable of identifying lightweight patches, it has proven to be not entirely reliable.
Meanwhile, since manufacturers do not currently provide information on the medical development characteristics of the patch materials, surgeons and radiologists either do not know the information at all or are forced to obtain the information through their own clinical experience, and sometimes even need to adopt a secondary exploration to directly view the postoperative condition of the lightweight patch in the patient. Therefore, for the lightweight patch with a wide application prospect, an effective and reliable medical imaging modality is not available at present to accurately detect the patch, and an objective and non-invasive diagnosis and evaluation method is not available to quantitatively characterize the postoperative characteristics of the patch, so that the improvement of the clinical utilization rate and the diagnosis and treatment level of the lightweight patch is greatly hindered. Therefore, a non-invasive analysis scheme is urgently needed to fill the gap in the technical field.
The references to which the present invention relates are as follows:
[1]Ciritsis A,Hansen N L,Barabasch A,et al.Time-Dependent Changes of Magnetic Resonance Imaging–Visible Mesh Implants in Patients[J].Investigative Radiology,49(7):439-444.
[2]Yu J,Wang Y,Shen Y.Noise reduction and edge detection via kernel anisotropic diffusion[J].Pattern Recognition Letters,29(10):1496-1503.
[3]Ciritsis A,Rossi C,Eberhard M,et al.Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making[J].European Radiology.
[4]Wu J,Wang Y,Yu J,et al.Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images[J].
[5]Kuehnert N,Kraemer N A,Otto J,et al.In vivo MRI visualization of mesh shrinkage using surgical implants loaded with superparamagnetic iron oxides[J].Surg Endosc,2012,26(5):1468-75.
[6]Kraemer N A,Donker H C W,Kuehnert N,et al.In Vivo Visualization of Polymer-Based Mesh Implants Using Conventional Magnetic Resonance Imaging and Positive-Contrast Susceptibility Imaging[J].Investigative Radiology,2013,48.
[7]Ozog Y,Konstantinovic M L,Werbrouck E,et al.Shrinkage and biomechanical evaluation of lightweight synthetics in a rabbit model for primary fascial repair[J],22(9):1099-1108.
[8]First In-Human Magnetic Resonance Visualization of Surgical Mesh Implants for Inguinal Hernia Treatment[J].Investigative Radiology,48(11):770-778.
[9]Slabu I,Guntherodt G,Schmitz-Rode T,et al.Investigation of Superparamagnetic Iron Oxide Nanoparticles for MRVisualization of Surgical Implants[J].Current Pharmaceutical Biotechnology,13(4):545-551.
disclosure of Invention
The invention aims to: aiming at the existing problems, the method for analyzing post-operation shrinkage of the in-vivo light-weight patch based on time change is provided, so that the problem that the light-weight patch is difficult to detect non-invasively is solved, and the post-operation shrinkage rate and movement condition of the implanted patch are accurately described and quantitatively analyzed.
The technical scheme adopted by the invention is as follows:
a method for post-operative shrinkage analysis of an in vivo lightweight patch based on time-varying, comprising:
A. the cross-section sequence and the sagittal plane sequence of the automatic three-dimensional breast ultrasound ABUS image at two time points are respectively processed as follows: (i.e., the transverse and sagittal planes at each time point are processed separately)
Preprocessing the data to separate out patch sequences of a cross section and a sagittal plane respectively; respectively constructing three-dimensional models of patch sequences in the cross section and the sagittal plane in two directions, and smoothing the constructed three-dimensional models; fusing the two three-dimensional models of the cross section and the sagittal plane;
wherein the two time points correspond to a state in which the patch has not been shrunken and a state in which the patch has been shrunken, respectively;
B. and correspondingly selecting the three-dimensional model with the minimum error rate between the three-dimensional models at two time points, and comparing the spatial variation difference between the two selected three-dimensional models so as to realize the analysis of post-operation shrinkage of the patch.
The method adopts a non-invasive detection mode, completes the quantitative analysis of post-operation shrinkage of the patch in vitro, and reduces the error rate of patch shrinkage through the construction of a scanning model and the fusion of the model, thereby realizing the accurate description of the shrinkage condition of the patch. Based on the method, the accurate diagnosis and evaluation of the postoperative complications and hernia recurrence rate of the implanted patch in the patient can be effectively assisted by doctors, and the blank of the current technical field is filled.
Further, the method for extracting the cross-sectional sequence and the sagittal plane sequence of the automated three-dimensional breast ultrasound ABUS images at the two time points comprises the following steps: automated three-dimensional breast ultrasound ABUS images at two time points are extracted at time intervals of 0.5mm for transverse and sagittal image sequences, respectively, to obtain a transverse sequence and a sagittal image sequence at each time point.
Further, the method for preprocessing the data to separate the patch sequences of the cross section and the sagittal plane respectively comprises the following steps: an image preprocessing method based on Matlab is adopted to preprocess the automatic three-dimensional breast ultrasound ABUS image sequences of two time points along two directions respectively, all images containing patches in the two directions of a cross section and a sagittal plane are selected respectively, the positions of the patches are marked in the selected images, and then the marked patches are separated from the corresponding images.
Further, the method for correspondingly selecting the three-dimensional model with the smallest error rate with the real patch from the three-dimensional models at two time points comprises the following steps:
and determining a time point in the two time points, respectively calculating the projection areas of the three-dimensional models of the time point, respectively calculating the error rate between the error rate and the corresponding projection area of the original patch corresponding to the time point, and selecting the three-dimensional model with the minimum error rate.
The method can determine the model closest to the real patch through simple calculation and comparison, and the subsequent comparison analysis is completed on the basis, and the difference analysis is completed by automatically comparing the states of the patch before and after shrinkage through a machine. It can be seen that this method has a great influence on the accuracy of the final analysis result.
Further, in step B, the method for aligning the spatially varying differences between the two selected three-dimensional models comprises: and comparing the wrinkle rate and the gravity center shift of the projection area between the two selected three-dimensional models.
Further, in step B, the method for aligning the spatially varying differences between the two selected three-dimensional models comprises: and visually displaying the spatial variation difference between the two selected three-dimensional models according to the corresponding relation between the preset display effect and the spatial variation condition.
Further, the preset corresponding relationship between the display effect and the spatial variation condition is as follows: and the corresponding relation between the preset color and the space variation condition.
Further, the two time points are the fifth day after operation and the ninety day after operation.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the invention can carry out high-accuracy reduction on the patch model, and can analyze the shrinkage condition of the patch visually and quantitatively through a corresponding tool, thereby reducing the difficulty of non-invasive detection of the lightweight patch, realizing accurate description and quantitative analysis on the shrinkage rate and the movement condition after the patch implantation, further effectively assisting doctors to accurately diagnose and evaluate the postoperative complications and hernia recurrence rate of the patch implantation in a patient body, and making up the blank of the current technical field.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of an assay method of the invention.
FIG. 2 is a schematic three-plane view (Axial: transverse; sagital: sagittal; coronal: coronal) of the human body for use with the ABUS data in the SIEMENS ABUS Workplace imaging workstation.
FIG. 3 is an example of the results of a three-dimensional visualization pre-study experiment of an in vivo lightweight patch based on time variation. (a) Ex vivo experimental box ABUS imaging results shown by three orthogonal plan views; (b) three-dimensional visualization of the ABUS imaging results.
Figure 4 is the abos three orthogonal plane imaging experimental results of an ex vivo lightweight patch.
FIG. 5 shows the ABUS test results and the patch detachment process for the area of the implanted patch in the patient. Wherein, (a) the result of the ABUS assay; (b) Matlab preprocessing results; (c) patch correct scaling results.
Figure 6 is a three-dimensional model of a patch constructed from the results of the ABUS test of the patch on day ninety in a patient.
FIG. 7 is an example of ABUS imaging of the incision area of a patient. (a) The patches (indicated by arrows in the figure) are shown in three orthogonal planes. (b) Coronal multi-slice views of implant patches
Fig. 8 is a graph of patch variance analysis based on time variation.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Example one
The embodiment discloses a post-operation shrinkage analysis method of an in-vivo lightweight patch based on time change, which comprises the following steps:
A. the cross-section sequence and the sagittal plane sequence of the automatic three-dimensional breast ultrasound ABUS image at two time points are respectively processed as follows:
preprocessing the data to separate out patch sequences of a cross section and a sagittal plane respectively; respectively constructing three-dimensional models of patch sequences in the cross section and the sagittal plane in two directions, and smoothing the constructed three-dimensional models; fusing the two three-dimensional models of the cross section and the sagittal plane;
wherein the two time points correspond to a state in which the patch has not shrunk and a state in which the patch has shrunk, respectively;
B. and correspondingly selecting the three-dimensional model with the minimum error rate with the real patch from the three-dimensional models (the cross section, the sagittal plane and the fused three-dimensional model) of the two time points, and comparing the spatial variation difference between the two selected three-dimensional models so as to realize the analysis of post-operation shrinkage of the patch. It should be noted that the three-dimensional models at the two selected time points necessarily correspond to each other, that is, are three-dimensional models in the same direction (as a fused three-dimensional model).
Example two
The embodiment discloses an in-vivo light weight type patch postoperative shrinkage analysis method based on time change, which comprises the steps of firstly extracting cross section (Axial) and sagittal plane (Sagital) sequences of an automatic three-dimensional Breast Ultrasound (ABUS) image of two time points of a patient, wherein the two time points are respectively the time points when a corresponding patch is not shrunk and the patch is shrunk; the data extracted at each time point are processed as follows:
preprocessing the data to separate out patch sequences respectively; respectively constructing and smoothing three-dimensional models of patch sequences in two directions (cross section and sagittal plane); and fusing the two three-dimensional models.
And comparing the space change difference between the fused three-dimensional models of the two time points, thereby realizing the quantitative analysis of post-operation shrinkage of the patch. The comparison result of the spatial variation difference can also be displayed visually, and usually, different spatial variation conditions (such as concave and convex variation of the patch, loss of the patch, no variation of the patch, and the like) can be set to display with different display effects, for example, different spatial variation conditions are set to display with different colors, so as to obtain a three-dimensional pseudo-color image corresponding to the spatial variation difference. Further, different colors can be set for different degrees of the same spatial variation.
It should be noted that the above-mentioned selection of comparing the spatial variation difference between the fused three-dimensional models at two time points, rather than comparing the spatial variation difference between the three-dimensional models in two directions at two time points, is because experiments prove that the fused three-dimensional model is closest to the structure of the real patch, i.e. the error rate of comparison is the smallest, so that the shrinkage analysis result is more accurate.
EXAMPLE III
The embodiment discloses a post-operation shrinkage analysis method of an internal lightweight patch based on time change, which comprises the following steps:
A. and respectively extracting the cross section and sagittal plane sequences of the automatic three-dimensional breast ultrasound ABUS image on two time points of the patient, wherein the two time points are respectively the time point when the corresponding patch is not shrunk and the time point when the patch is shrunk.
B. The method comprises the steps of preprocessing ABUS image sequences of two time points along two directions of a cross section and a sagittal plane respectively by adopting an image preprocessing method based on Matlab, automatically selecting all images containing patches, marking the positions of the patches in the corresponding images, and finally separating the patches from each slice image.
C. And respectively constructing three-dimensional models of the separated patches at two time points by taking the transverse section and the sagittal plane as references. I.e. two three-dimensional models corresponding to the transverse and sagittal planes are constructed for each time point, respectively.
D. And respectively carrying out polygonal surface smoothing on the three-dimensional models in two directions (cross sections and sagittal planes) of two time points, and fusing the three-dimensional models by taking the cross sections and the sagittal planes as references after the smoothing.
E. And calculating the projection areas of the two three-dimensional models after smoothing treatment and the projection area of the three-dimensional model after fusion of one time point in the two time points, and comparing the projection area of each three-dimensional model of the selected time point with the projection area of the original patch in the corresponding direction (the transverse section direction, the sagittal plane direction and the direction of the three-dimensional model after fusion) at the time point to calculate the corresponding error rate. The time point selected in this step need not be specified, but only the selected patch needs to correspond to the selected time point.
F. From the error rate calculation in each direction, a three-dimensional model with the smallest error rate is determined, and is set as an X model here.
G. Comparing the spatial variation difference between the X models of two time points, and performing deviation analysis: and calculating the wrinkle rate and the gravity center shift condition of the projection area between the X models of the two time points.
Further, the method further comprises H: and G, outputting a three-dimensional pseudo-color image corresponding to the deviation analysis result in the step G to display the concave-convex deformation condition after the patch implantation.
According to clinical evidence, the patch implanted by the wounded person does not deform on the fifth postoperative day, and the surgical site of the wounded person recovers on the ninety postoperative day, and the patch is shrunk. Therefore, the two time points can be taken from the fifth and the ninety days after the operation of the wounded.
Example four
As shown in fig. 1, the present embodiment discloses a post-operation shrinkage analysis method of an in-vivo lightweight patch based on time variation, which comprises the following steps:
A. automated three-dimensional breast ultrasound ABUS images of the patient on the fifth and ninety days (corresponding to shrinkage of the patch) were sequenced in transverse and sagittal planes at intervals of every 0.5mm, respectively. The experiment must take two time points to compare to analyze the shrinkage of the patch over time.
B. And (3) preprocessing the ABUS image sequences at two time points respectively along the two directions of the cross section and the sagittal plane by adopting an image preprocessing method based on Matlab, automatically selecting all images containing the patches, marking the positions of the patches in the images by using color lines, and finally separating the patches from each slice image.
C. And constructing three-dimensional models of the cross section and the sagittal plane of the patch sequences of the fifth day and the ninety day respectively, namely constructing two three-dimensional models by the patch of the fifth day and constructing two three-dimensional models by the patch of the ninety day.
D. And (3) smoothing the three-dimensional models in the two directions (the transverse plane and the sagittal plane) of the fifth day and the ninety day respectively by using medical three-dimensional modeling software mimics, and fusing the two polygonal three-dimensional models (corresponding to the transverse plane and the sagittal plane) after smoothing.
E. Calculating the projection area of the cross section and sagittal plane three-dimensional model of the fifth day or the ninety day by using three-dimensional reverse engineering detection software Geomagic Wrap, comparing the projection area with the projection area of the patch corresponding to the time point, and calculating the error rate; and calculating the projection area of the model after the two directions are fused, comparing the projection areas of the patches corresponding to the time points, and calculating the error rate.
F. And E, determining and outputting the three-dimensional model X with the minimum error rate according to the error rate calculated in the step E.
G. And taking the three-dimensional model X on the fifth day as a target entity, taking the three-dimensional model X on the ninety day as an entity, and performing difference analysis on the two models to generate a three-dimensional pseudo-color image. Wherein blue indicates the area of the patch where the concave change occurs; green represents the area where no change in patch surface occurs; yellow to red areas indicate the patch is convex; grey indicates loss of patch.
EXAMPLE III
Referring to FIG. 1, this example discloses a time-based method for post-operative shrinkage analysis of an in vivo lightweight patch. The post-operation shrinkage analysis method of the internal lightweight patch based on time change of the embodiment comprises the following steps:
1. and extracting transverse and sagittal plane images. FIG. 2 shows a schematic representation of three essential medical sections of the human body (Axial: transverse; sagital: sagittal; coronal: coronal); figure 4 is a result of ABUS three-plane imaging of an ex vivo lightweight patch. As can be seen from the figure: the new ABUS Coronal Plane (Coronal Plane) view clearly shows the lightweight patch.
2. Separation of the ultrasound image cross-sectional and sagittal patches. The experimental results are shown in fig. 5 (c):
(1) Extracting 318 image sequences of the cross section of the ultrasonic image and 388 image sequences of the sagittal plane of the ultrasonic image, wherein the experimental result is shown in fig. 5 (a);
(2) Finding the position of the patch in each slice, and separating the patch in each slice by using an image preprocessing method based on Matlab, wherein the experimental result is shown in FIG. 5 (b);
(3) The actual shape of the patch is restored by back-filling each image, and the pixels of each image are adjusted, and the experimental result is shown in fig. 5 (c).
3. And establishing a dimensional model for the actual patch.
The sequence of pictures from the group of fig. 5 (c) was fused into a new three-dimensional model, the result of which is shown in fig. 6.
FIG. 3 is an example of the results of a three-dimensional visualization pre-study experiment of an in vivo lightweight patch based on time variation. (a) Ex vivo experimental box ABUS imaging results shown by three orthogonal plan views; (b) three-dimensional visualization of the ABUS imaging results. As shown in the figure, the in-vitro experiment performed by using the platform provides effective guarantee for the three-dimensional visualization of the in-vitro experiment box, so that the three-dimensional visualization effect of the fine light-weight mesh-shaped texture of the patch is achieved.
4. As can be seen in FIG. 7, the new ABUS Coronal/surgical Plane (Coronal Plane) view clearly shows the mesh texture of the lightweight patch (as indicated by the arrows in the figure) and the wavy contour of the patch due to post-operative shrinkage. The depth of the ABUS scan is 20mm. (a) The patches (indicated by arrows in the figure) are shown in three orthogonal planes. The corresponding depth of the current coronal view is 13.1mm. Note that the implant patch is accurately visible in the coronal and transverse planes, but only indirectly recognizable in the sagittal plane. (b) Coronal multi-slice views of the implant patch with slice spacing of 0.5mm. The net-like texture of the patch and the wavy contour of the patch due to post-operative shrinkage are clearly shown.
5. And (5) analyzing deviation. As can be seen in fig. 8, time-varying based in vivo lightweight patch shrinkage has been described and quantified. The colored areas represent local changes in the patch in the body. Blue indicates concave changes; green represents the patch area with unchanged surface; the yellow to red areas indicate the occurrence of the saliency. The loss of the surface patch is visible by dark grey.
The postoperative shrinkage rate degree of the patch reflects the activity degree of inflammatory reaction, and is a key index for evaluating hernia recurrence risk. Through the quantitative analysis of the post-operation characteristics of the light-weight patch, the accurate and quantitative assessment of the post-operation wrinkle rate of the light-weight patch is realized. The problem that the patch is easily affected by spatial transformation such as inclination, curling and shrinkage during artificial evaluation of the wrinkle rate of the postoperative patch is solved, and therefore accurate evaluation of hernia recurrence risk is achieved.
In all cases, ABUS clearly showed a lightweight patch. In this embodiment, all patches are successfully constructed and the projected areas of all patches are calculated. In the case of segmentation based on only one image direction (transverse or sagittal), the segmentation results deviate significantly from the actual patch size, especially in the case of heavy folding (up to 22.7%), see table 1 for details.
When considering cutting the image from two directions, the true area of the patch is highly correlated with the segmentation result, independent of the folding state. The maximum deviation of the measured values from the actual dimensions was only 3.6%.
In this example, eleven patients' patches were successfully separated from the surrounding tissue and a three-dimensional polygonal model was constructed with reference to two time points. On the fifth day, the mean surface area of the patch implant was 108.9cm 2 Patch size is from 79cm 2 To 126.2cm 2 (ii) a On day ninety, the average surface area of the patch implant was 86.9cm 2 Patch size is from 63cm 2 To 112cm 2 . The average shrinkage from day five to day ninety was 22cm2 (ranging from 12 cm) 2 -32.7cm 2 ) Or 20.2% (ranging from 9.7% to 29.4%), for details see table 2. The mean centroid shift on days five and ninety was 0.23cm (ranging from 0.19cm to 0.27 cm).
In the case of incisional hernia surgery, it is common to have a distance of 3 to 5cm between the edge of the patch and the center of the incisional hernia. The present invention demonstrates for the first time that in order to obtain the best surgical result, an average mesh reduction of 20.2% must be considered.
As can be seen from tables 1 and 2, time-dependent in vivo lightweight patch shrinkage can be quantified. The method not only effectively quantifies the shrinkage and the mobility of the patch, but also better maintains the texture information and the edge characteristic of the ultrasonic image. The ultrasonic image based on the method of the invention obtains better experimental results.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification, and to any novel method or process steps or any novel combination of steps disclosed.
Claims (6)
1. A method for post-operative shrinkage analysis of an in vivo lightweight patch based on time-varying, comprising:
A. the cross-section sequence and the sagittal plane sequence of the automatic three-dimensional breast ultrasound ABUS image at two time points are respectively processed as follows:
preprocessing the data to separate out patch sequences of a cross section and a sagittal plane respectively; respectively constructing three-dimensional models of patch sequences in the cross section and the sagittal plane in two directions, and smoothing the constructed three-dimensional models; fusing the two three-dimensional models of the cross section and the sagittal plane;
wherein the two time points correspond to a state in which the patch has not been shrunken and a state in which the patch has been shrunken, respectively;
B. correspondingly selecting the three-dimensional model with the minimum error rate with the real patch from the three-dimensional models of the two time points, wherein the three-dimensional model comprises the following steps: determining a time point in the two time points, respectively calculating the projection areas of the three-dimensional models of the time point, respectively calculating the error rate between the error rate and the corresponding projection area of the original patch corresponding to the time point, and selecting the three-dimensional model with the minimum error rate;
comparing the spatially varying differences between the two selected three-dimensional models to enable analysis of post-patch shrinkage, comprising: and comparing the wrinkle rate and the gravity center shift of the projection area between the two selected three-dimensional models.
2. The time-variant-based intra-body lightweight patch post-operative shrinkage analysis method of claim 1, wherein the extraction method of the cross-sectional sequence and the sagittal-plane sequence of the automated three-dimensional breast ultrasound ABUS images at the two time points comprises: automated three-dimensional breast ultrasound ABUS images of two time points are respectively extracted into image sequences of a transverse plane and a sagittal plane at each time interval of 0.5mm to obtain a transverse plane sequence and a sagittal plane sequence of each time point.
3. A method for post-surgical shrinkage analysis of a time-varying in vivo lightweight patch according to claim 1, wherein the method of preprocessing the data to isolate cross-and sagittal-plane patch sequences, respectively, comprises: an image preprocessing method based on Matlab is adopted to preprocess the automatic three-dimensional breast ultrasound ABUS image sequences of two time points along two directions respectively, all images containing patches in the two directions of a cross section and a sagittal plane are selected respectively, the positions of the patches are marked in the selected images, and then the marked patches are separated from the corresponding images.
4. The method for post-surgical wrinkle analysis based on time-varying internal lightweight patch according to claim 1, wherein in step B, the method for aligning the spatially varying differences between the two selected three-dimensional models comprises: and visually displaying the spatial variation difference between the two selected three-dimensional models according to the corresponding relation between the preset display effect and the spatial variation condition.
5. The method for post-operative shrinkage analysis of a time-varying, in vivo lightweight patch according to claim 4, wherein the predetermined correspondence between the demonstration effect and the spatial variation is: and the corresponding relation between the preset color and the space change condition.
6. The method for analyzing post-operative shrinkage of a time-varying internal lightweight patch according to claim 1~5, wherein said two time points are fifth and ninety days after surgery, respectively.
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