CN108416769B

CN108416769B - IVOCT image vulnerable plaque automatic detection method based on preprocessing

Info

Publication number: CN108416769B
Application number: CN201810174574.9A
Authority: CN
Inventors: 刘然; 张艳珍; 田逢春; 郑杨婷; 李德豪; 刘明明
Original assignee: Chengdu Seefeld Technology Co ltd; Chongqing University
Current assignee: Chengdu Seefeld Technology Co ltd; Chongqing University
Priority date: 2018-03-02
Filing date: 2018-03-02
Publication date: 2021-06-04
Anticipated expiration: 2038-03-02
Also published as: CN108416769A

Abstract

The invention discloses an IVOCT image vulnerable plaque automatic detection method based on preprocessing, which comprises the steps of firstly collecting a positive sample IVOCT image containing vulnerable plaque and a negative sample IVOCT image without vulnerable plaque, converting the sample IVOCT image from a Cartesian coordinate system to a polar coordinate system and carrying out denoising treatment, carrying out head-to-tail splicing on each IVOCT image, extracting the images by adopting a sliding window with the size of the original image in the spliced IVOCT image and obtaining a mirror image of the extracted images, then carrying out head-to-tail splicing on each image so as to enhance sample data, training a Faster R-CNN network by adopting an enhanced training sample set, carrying out vulnerable plaque detection on the IVOCT image to be detected by adopting the Faster R-CNN network obtained by training, carrying out overlapping vulnerable plaque area treatment on the detected image, and then carrying out coordinate system restoration. The method can effectively improve the technical performance of automatic detection of vulnerable plaques of the IVOCT image, has objective results, and can greatly reduce the workload of doctors.

Description

IVOCT image vulnerable plaque automatic detection method based on preprocessing

Technical Field

The invention belongs to the technical field of IVOCT image processing, and particularly relates to an IVOCT image vulnerable plaque automatic detection method based on preprocessing.

Background

Coronary heart disease (CAD) is one of the most fatal diseases in the world. Among them, Acute Coronary Syndrome (ACS) is the most dangerous. Whereas nearly 70% of ACS events are caused by rupture of the vulnerable plaque (vulneable plaque) of coronary atherosclerosis (coronary atherosclerosis). Coronary atherosclerotic vulnerable plaque is therefore the major culprit leading to ACS and therefore early detection of vulnerable plaque and active intervention is required.

Detection of vulnerable plaque relies on intravascular imaging techniques (intravascular imaging modality). At present, two imaging techniques, namely intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT), are commonly used clinically to detect vulnerable plaques. Among them, IVOCT is a high resolution (10-20 μm) imaging modality, about 10 times that of IVUS. Studies by Kume and Kubo et al show that IVOCT has better sensitivity (sensitivity) and specificity (specificity) for detection of vulnerable plaques compared to IVUS. In addition, the technology can perform repeated processing, and the stability of the result can be still ensured after multiple times of imaging. Therefore, the IVOCT technique is more suitable for the detection of vulnerable plaque.

The traditional method for detecting vulnerable plaque based on IVOCT image is that doctors judge by naked eyes according to own experience, the process is time-consuming and labor-consuming, and the result subjectivity is strong. In this context, there is a need to enable automated detection of atherosclerotic vulnerable plaque. In previous studies, a method of computer-assisted Fibrous Cap (FC) volume analysis was proposed in "Wang, Z., et al, Volumetric quantification of fibrous caps using intravascular optical coherence Express,2012.3(6): p.1413-1426" to analyze vulnerable plaques; thereafter, they have proposed a logistic regression model as a quantitative diagnostic model to significantly simplify the diagnosis of vulnerable plaque. An IVOCT image coronary artery pulse atherosclerotic disease detection system is given in Xu, M, et al, automatic image classification in an intravascular optical coherence imaging in Region 10 reference.2017, which first preprocesses the IVOCT image and then detects unhealthy subjects using a linear SVM classifier. On the basis, the image data is added, the preprocessing of the IVOCT image and a Support Vector Machine (SVM) classifier are improved, and the classification is finer and more accurate.

However, the above research work still adopts the conventional image classification (image classification) or object detection (object detection) method, only the accuracy (precision rate) of classification or detection is considered, and indexes such as recall rate (recall rate) and overlap rate (overlap rate) are not considered, which leads to difficult clinical popularization.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide an IVOCT image vulnerable plaque automatic detection method based on preprocessing, so that the IVOCT image vulnerable plaque automatic detection is accurately realized, and the workload of a doctor is saved.

In order to achieve the aim, the IVOCT image vulnerable plaque automatic detection method based on pretreatment comprises the following steps:

s1: collecting K sample IVOCT images, wherein the K sample IVOCT images comprise a positive sample IVOCT image containing vulnerable plaques and a negative sample IVOCT image without the vulnerable plaques, normalizing the size of the IVOCT images to a preset size, and manually marking vulnerable plaque areas in each positive sample IVOCT image;

s2: converting each sample IVOCT image from a Cartesian coordinate system to a polar coordinate system, and recording the size of the IVOCT image under the polar coordinate system as MXN;

s3: denoising each sample IVOCT image under the polar coordinate;

s4: respectively splicing the denoised sample IVOCT images end to obtain spliced IVOCT images of Mx 2N, and merging the images into a vulnerable plaque if vulnerable plaques exist on the left side and the right side of the original IVOCT image during splicing and the spliced IVOCT images are connected;

in the spliced IVOCT images, sliding the window with the size of M multiplied by N according to a preset step length to extract the images, recording the number of the obtained IVOCT images as H, then carrying out horizontal mirror image processing on the H IVOCT images to obtain H mirror image IVOCT images, and obtaining 2H IVOCT images in total; respectively splicing the 2H IVOCT images obtained from each sample IVOCT image end to obtain 2H spliced IVOCT images, and combining the 2H spliced IVOCT images into a vulnerable plaque if vulnerable plaques exist on the left side and the right side of the original IVOCT image during splicing and the IVOCT images obtained by splicing are connected;

k pieces of sample IVOCT images are summed to obtain K multiplied by 2H pieces of spliced IVOCT images which are used as a training sample set after data enhancement;

s5: for each spliced IVOCT image in the Kx 2H spliced IVOCT images obtained in the step S4, if a vulnerable plaque exists, the spliced IVOCT image is a positive sample, the label of the spliced IVOCT image is set to be 1, the position information of the spliced IVOCT image, the label and the vulnerable plaque area forms training data, otherwise, the spliced IVOCT image is a negative sample, the label of the spliced IVOCT image is set to be 0, the spliced IVOCT image, the label and the position information of the whole spliced IVOCT image area form training data, so that a training data set is obtained, and then the obtained training data set is converted into a format suitable for fast R-CNN network input;

s6: training in a Faster R-CNN network by adopting the training data set obtained in the step S5 to obtain a detection model;

s7: when vulnerable plaque detection needs to be performed on the IVOCT image, firstly, the size of the IVOCT image to be detected is adjusted to be a preset normalized size, then denoising processing is performed, the obtained IVOCT image is spliced end to end, the spliced IVOCT image is input into the detection model obtained in the step S6 for detection, and the vulnerable plaque area is marked in the spliced IVOCT image;

s8: for the spliced IVOCT image output in the step S7, if the overlapped vulnerable plaque areas exist, combining the overlapped vulnerable plaque areas to be used as a vulnerable plaque area, otherwise, not doing any operation; splicing the IVOCT image after the overlapped vulnerable plaque area is processed, and intercepting an image of the left half part of the image, namely M multiplied by N, as a detection result;

s9: and converting the detection result of the step S8 from the polar coordinate system to a Cartesian coordinate system to obtain a vulnerable plaque detection result of the IVOCT image to be detected.

The invention relates to an IVOCT image vulnerable plaque automatic detection method based on preprocessing, which comprises the steps of firstly collecting a positive sample IVOCT image containing vulnerable plaque and a negative sample IVOCT image without the vulnerable plaque, converting the sample IVOCT image from a Cartesian coordinate system to a polar coordinate system and carrying out denoising treatment, carrying out head-to-tail splicing on each IVOCT image, extracting the images by adopting a sliding window with the size of the original image in the spliced IVOCT image and obtaining a mirror image of the extracted images, then carrying out head-to-tail splicing on each image so as to enhance sample data, training a fast R-CNN network by adopting an enhanced training sample set, carrying out vulnerable plaque detection on the IVOCT image to be detected by adopting the fast R-CNN network obtained by training, carrying out overlapping plaque area treatment on the detected image, and then carrying out coordinate system restoration. The invention adopts the Faster R-CNN network and performs data enhancement pretreatment on the sample IVOCT image, thereby effectively improving the technical performance of automatic detection of vulnerable plaques of the IVOCT image, leading the result to have objectivity and greatly reducing the workload of doctors.

Drawings

FIG. 1 is a flowchart of an embodiment of an IVOCT image vulnerable plaque automatic detection method based on preprocessing according to the invention;

FIG. 2 is an exemplary diagram of a positive sample IVOCT image in this embodiment;

FIG. 3 is a transformation of the positive sample IVOCT image of FIG. 2 to an image in a polar coordinate system;

FIG. 4 is an exemplary illustration of the IVOCT image removal imaging catheter calibration loop and imaging catheter in this embodiment;

FIG. 5 is an exemplary illustration of noise above a vessel wall;

FIG. 6 is a diagram of an example of detection of an upper edge of a vessel wall;

FIG. 7 is an exemplary diagram of the IVOCT image removing noise in the region above the vessel wall in the present embodiment;

FIG. 8 is an exemplary diagram of IVOCT image stitching;

fig. 9 is an exemplary diagram of the stitched IVOCT image of fig. 8 using a sliding window to extract the image and then stitching.

Detailed Description

The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.

Examples

Fig. 1 is a flowchart of an embodiment of the method for automatically detecting vulnerable plaque based on a preprocessed IVOCT image. As shown in fig. 1, the method for automatically detecting vulnerable plaque based on preprocessed IVOCT images of the present invention specifically includes the following steps:

s101: acquiring a sample IVOCT image:

collecting K sample IVOCT images, wherein the K sample IVOCT images comprise a positive sample IVOCT image containing vulnerable plaques and a negative sample IVOCT image without the vulnerable plaques, normalizing the size of the IVOCT images to a preset size, and manually marking vulnerable plaque areas in each positive sample IVOCT image.

In this embodiment, the number of positive sample IVOCT images and negative sample IVOCT images is 1000, and the image size is 704 × 704(height × width), which is a single-channel 8-bit grayscale image. Fig. 2 is an exemplary diagram of a positive sample IVOCT image in the present embodiment. As shown in fig. 2, the imaging catheter, the guide wire and the inner wall of the blood vessel during the IVOCT imaging process are marked by characters and arrows, a sector area is a vulnerable plaque area manually marked by a doctor, two vulnerable plaques a and B exist in the IVOCT image shown in fig. 2, and the vulnerable plaque area is represented by image pixel coordinates in the x-axis direction.

S102: and (3) coordinate system transformation:

and converting each sample IVOCT image from a Cartesian coordinate system to a polar coordinate system so as to facilitate subsequent processing, and recording the size of the IVOCT image under the polar coordinate system as MxN. The image size in the polar coordinate system after the conversion is 352 × 720(height × width) in this embodiment. Fig. 3 is an image of the positive sample IVOCT image of fig. 2 transformed into a polar coordinate system. As shown in FIG. 3, A and B are vulnerable plaques, C is the imaging catheter, and D is noise above the vessel wall.

S103: denoising the IVOCT image:

and denoising each sample IVOCT image under the polar coordinate. The specific method of denoising can be selected as required.

Through analyzing the IVOCT image under the polar coordinate, the noise that influences the testing result greatly in the IVOCT image includes the noise above imaging catheter calibration circle, imaging catheter and the vascular wall, to these three kinds of noise, has designed the denoising method in this embodiment, and its concrete method includes:

1) removing the imaging catheter calibration loop and the imaging catheter:

since the calibration circle of the imaging catheter is fixed in position in each image and is distributed above and at the bottom of the image, all the pixel values corresponding to the calibration circle of the imaging catheter are directly assigned with zero values in the embodiment. Since the imaging catheter is particularly obvious and linear, it is easy to affect the effect of detecting the edge of the blood vessel wall, so that the pixel values corresponding to the partial region of the imaging catheter are all zero, the range of the partial region of the imaging catheter is set according to the actual situation, and the partial regions of the imaging catheter of the 1 st to the 25 th behaviors are set in the embodiment. Fig. 4 is an exemplary diagram of the IVOCT image removing imaging catheter calibration circle and edge noise on the image in the present embodiment. The imaging catheter calibration loop and imaging catheter may be eliminated by employing a pixel zeroing approach, as shown in fig. 4.

2) Vessel wall top noise removal

Fig. 5 is an exemplary view of noise above a blood vessel wall. As shown in the two IVOCT images in fig. 5, the blood vessel wall has various shapes, and the oval portion is a noise region above the blood vessel wall. For this part of the noise, the following method is adopted in this embodiment to remove:

1) detecting the upper edge of the vessel wall:

first, the upper edge of the vessel wall is detected, and the method adopted in this embodiment is as follows: firstly, converting a gray level image of an IVOCT image into a binary image; performing mathematical morphology operation on the binary image: performing multiple expansion operations and then performing multiple corrosion operations, and taking the highest point of each row of white pixels to obtain a rough edge result; and then, fitting by adopting a high-order function, and representing the upper edge of the binary image by using a curve. Fig. 6 is a diagram showing an example of detection of an upper edge of a blood vessel wall. As shown in fig. 6, curve a is the rough edge result and curve b is the fitted edge result. However, the ordinate value of the fitted curve is floating point type data, and the column of the IVOCT image gray scale image is an integer value, so the ordinate of the curve is rounded. Because the upper edge of the blood vessel wall needs to be fitted, in order to prevent the loss of the blood vessel wall edge information while removing noise, the fitted edge curve is translated upwards by a certain distance to obtain a curve c, so that the blood vessel wall edge information is kept as much as possible.

2) Removing noise from the upper edge of a vessel wall

In the vulnerable plaque detection, the required information is the information below the edge of the blood vessel wall, and the information on the upper edge of the blood vessel wall is not required, so that the area above the upper edge of the blood vessel wall is filled with black, namely, the pixel values of the area are set to zero. Fig. 7 is an exemplary diagram of the above-blood-vessel-wall noise removal result.

S104: and (3) IVOCT image data enhancement:

because the sample size of the IVOCT image is less, the fast R-CNN network used in the subsequent detection needs a large amount of data when the detection model is trained, and therefore, the IVOCT image data needs to be enhanced.

For better data enhancement, it is necessary to analyze the vulnerable plaque location and size in IVOCT images. In the positive sample IVOCT image, some vulnerable plaques are positioned at the left and right side edges of the image, the vulnerable plaque area is relatively narrow, in order to reduce the influence of the over-narrow plaques on the detection result, in the invention, each denoised sample IVOCT image is spliced from head to tail to obtain an M multiplied by 2N spliced IVOCT image, and if the vulnerable plaques exist at the left and right sides of the original IVOCT image during splicing and the spliced IVOCT images are connected, the vulnerable plaques are combined.

Fig. 8 is an exemplary diagram of IVOCT image stitching. As shown in fig. 8, in the positive sample IVOCT image, there are two vulnerable plaques located at the left and right edges of the image, respectively, and the vulnerable plaque area on the right side is very narrow, and the IVOCT images are connected together end to generate a spliced IVOCT image with a size of 352 × 1440(height × width). Thus, the vulnerable plaques B and A' on the left and right edges are spliced together and merged into a vulnerable plaque C, and are located in the middle area of the whole image. Because the upper edge of the vessel wall is a closed circle in a Cartesian coordinate system, the topological invariance between the pixel points can still be kept by adopting the head-to-tail splicing method after the vessel wall is converted into a polar coordinate system.

In the spliced IVOCT images, sliding is carried out on a window with the size of M multiplied by N according to a preset step length to extract images, the number of the obtained IVOCT images is recorded to be H, then H pieces of IVOCT images are horizontally mirrored to obtain H mirror image IVOCT images, and 2H pieces of IVOCT images are obtained in total. And (3) respectively carrying out head-to-tail splicing on the 2H IVOCT images obtained from each sample IVOCT image to obtain 2H spliced IVOCT images, and similarly, if vulnerable plaques exist on the left side and the right side of the original IVOCT image during splicing and the IVOCT images obtained by splicing are connected, merging the vulnerable plaques into one. K pieces of sample IVOCT images are summed to obtain K multiplied by 2H pieces of spliced IVOCT images which are used as a training sample set after data enhancement.

Fig. 9 is an exemplary diagram of the stitched IVOCT image of fig. 8 using a sliding window to extract the image and then stitching. As shown in fig. 9, in this example, 10 IVOCT images with the size of 352 × 720 are obtained by using a sliding window in the spliced IVOCT image shown in fig. 8, each extracted IVOCT image includes a vulnerable plaque region, and all 10 IVOCT images are spliced end to obtain 10 spliced IVOCT images with the size of 352 × 1440.

S105: generating training data:

for each spliced IVOCT image in the Kx 2H spliced IVOCT images obtained in the step S104, if a vulnerable plaque exists, the spliced IVOCT image is a positive sample, the label of the spliced IVOCT image is set to be 1, the position information of the spliced IVOCT image, the label and the vulnerable plaque area forms training data, otherwise, the spliced IVOCT image is a negative sample, the label of the spliced IVOCT image is set to be 0, the spliced IVOCT image, the label and the position information of the whole spliced IVOCT image area form training data, so that a training data set is obtained, the obtained training data set is converted into a format suitable for fast R-CNN network input, and the data set in the PASCAL VOC 2007 format is manufactured in the embodiment.

Taking the stitched image shown in fig. 8 as an example of a positive sample, the training data is as follows:

0001.png,1,1 140,698 860,1418 1440；

png is a file name field; 1, representing a positive sample; 1140, which represents 1-140 (unit: pixel) in the x-axis direction of the image as the first vulnerable plaque area; 698860, representing 698-860 in the x-axis direction of the image as a second vulnerable plaque area; 14181440 show 1418-1440 as the third vulnerable plaque area in the x-axis of the image.

An example of training data for negative examples is as follows:

1001.png,0,1 1440；

png is a file name field; 0, representing a negative sample, indicates that the entire image contains no vulnerable plaque. 1

1440 represents the entire stitched image.

S106: training the Faster R-CNN network:

and (5) training in the Faster R-CNN network by adopting the training data set obtained in the step (S105) to obtain a detection model.

The Faster R-CNN Network is provided on the basis of R-CNN and Fast R-CNN, a Region suggestion Network (RPN) is designed by the model to generate suggestion regions, the original Selective Search method is replaced, the CNN generating the suggestion regions is shared with the CNN of target detection, a large amount of time is saved, and the overall structure of the Faster R-CNN Network is equivalent to the combination of RPN + Fast R-CNN. A detailed description of the fast R-CNN network can be found in the literature "Ren, S., et al", fast R-CNN: Towards Real-Time Object Detection with Region pro-technical networks. IEEE Transactions on Pattern Analysis & Machine Analysis, 2017.39(6): p.1137-1149. The invention adopts the fast R-CNN network to automatically detect the vulnerable plaque of the IVOCT image, can save the time and the cost of a cardiologist, and reduces the subjectivity.

S107: vulnerable plaque detection:

when vulnerable plaque detection needs to be performed on the IVOCT image, the size of the IVOCT image to be detected is adjusted to be a preset normalized size, then denoising processing is performed, the obtained IVOCT image is spliced end to end, and the spliced IVOCT image is input into the detection model obtained through training in the step S106 for detection. When vulnerable plaques exist in the spliced IVOCT image, the fast R-CNN network detection model marks the areas of the vulnerable plaques in the spliced IVOCT image.

S108: and (3) processing an overlapping vulnerable plaque area:

in a stitched IVOCT image where vulnerable plaque regions are detected, when there are multiple vulnerable plaque regions, there may be overlap between vulnerable plaques. Therefore, for the stitched IVOCT image output in step S107, if there are overlapping vulnerable plaque regions, the overlapping vulnerable plaque regions are merged as one vulnerable plaque region, otherwise, no operation is performed, that is, when there is no vulnerable plaque region in the output stitched IVOCT image, or there is no overlap although the vulnerable plaque regions are detected, there is no need to merge the overlapping vulnerable plaque regions. And (3) splicing the IVOCT images after overlapping the vulnerable plaque area, and cutting the image of the left half part M multiplied by N as an output result.

S109: and (3) coordinate system reduction:

and converting the output result of the step S108 from the polar coordinate system to a Cartesian coordinate system to obtain a vulnerable plaque detection result of the IVOCT image to be detected.

In order to illustrate the technical effects of the present invention, a specific example was used for experimental verification. The relevant parameters of the experiment platform of the experiment are as follows: the operating system is 64-bit Windows10 flagship edition, the capacity of the hard disk is 1T, the processor is Intel (R) core (TM) i7-6800K CPU @3.40GHz 3.40GHz, the video card is NVIDIA GTX1080, the memory is 64.0GB, the pre-installed software comprises Matlab2015b 64 bit, Visual Studio Ultimate 2013 and 64-bit Anaconda2, and a Faster R-CNN network is built based on a Caffe framework. Faster R-CNN provides three well-trained models: small ZF, medium VGG _ CNN _ M _1024 and large VGG 16. In the present embodiment, a VGG _ CNN _ M _1024(end-to-end) model is adopted in consideration of the sample size.

In this experimental verification, the sample IVOCT images include 1000 positive sample IVOCT images and 1000 negative sample IVOCT images, and the image size is 352 × 720(height × width), which is a single-channel 8-bit grayscale image. The number of IVOCT images used for the test was 300.

When the detection performance is evaluated, according to the characteristics of vulnerable plaques of the IVOCT image, the detection result of the vulnerable plaques is evaluated by adopting the following method in the experiment:

setting the set of real vulnerable plaques in the IVOCT image to be tested as { A₁,A₂,…,A_PIn which A is_pRepresenting the area range of the P-th real vulnerable plaque, wherein P is 1,2, …, and P represents the number of real vulnerable plaques; the actually detected vulnerable plaque set is { B₁,B₂,…,B_QIn which B is_qRepresents the area range of the qth detected vulnerable plaque, Q is 1,2, …, Q represents the number of detected vulnerable plaques. Initializing a statistical variable N₁、N₂、N₃Are all 0.

And for Q vulnerable plaques, firstly, judging whether intersection exists in the area ranges of the vulnerable plaques, if so, deleting the vulnerable plaques from the vulnerable plaque detection set, wherein the two vulnerable plaques are error results. Then traversing each detected vulnerable plaque in the detected vulnerable plaque set, comparing the detected vulnerable plaque with each real vulnerable plaque in the real vulnerable plaque set, and if the area range of the qth detected vulnerable plaque and the area range of the pth real vulnerable plaque have intersection, calculating the correlation DSC (p, q) by adopting the following formula:

if DSC (p, q) is greater than 0.5, then the q-th detected vulnerable plaque is considered to be a correct result, let N₁＝N₁+ 1; if the area range of the qth detected vulnerable plaque and the area range of the pth real vulnerable plaque have intersection, but DSC (p, q) is less than or equal to 0.5, or the area range of the qth detected vulnerable plaque and any real vulnerable plaque have no intersection, let N be₂＝N₂+1。

Traversing each real vulnerable plaque in the real vulnerable plaque set, if no intersection exists with any area range for detecting vulnerable plaque, the real vulnerable plaque is missed for detection, and enabling N₃＝N₃+1。

The recall rate R of vulnerable plaque detection is calculated as follows:

the above equation shows that if the real vulnerable plaque does not appear in the final detection result, the detection is missed, and the less vulnerable plaque is missed, the higher the recall rate is.

The calculation formula of the vulnerable plaque detection accuracy rate G is as follows:

the above formula indicates that the detected vulnerable plaque and the real vulnerable plaque have an intersection, and the DSC value is less than or equal to 0.5, or the detected vulnerable plaque and the real vulnerable plaque have no intersection, the false detection is performed, the less the false detection is performed, and the higher the accuracy is;

the calculation formula of the coincidence rate D of vulnerable plaque detection is as follows:

the above equation illustrates that the degree of overlap D for vulnerable plaque detection is the average of all correlations greater than 0.5, i.e., DSC_nIs the nth correlation value greater than 0.5. The degree of coincidence takes into account the accuracy of the detection result in the case of correct detection.

The calculation formula of the detection quality factor S for vulnerable plaque detection is as follows:

D＝0.5*(2*G*R/(G+R))+0.5*D

wherein the larger the S value, the better the detection result. From the above equation, recall and accuracy are two factors that constrain each other, the combination of these two terms takes a weight of 0.5, and the degree of overlap takes a weight of 0.5.

In the experimental verification, the key parameters of the Faster R-CNN are set as follows: confidence THRESHOLD (CONF _ THRESHOLD) 0.7; number of iterations (iters):80 k; a non-maximum threshold (NMS _ THRESH) of 0.3; base _ lr of 0.0001; step size (stepsize): 2000.

In order to better embody the technical advantages of the invention, experiments are designed aiming at negative sample label setting and data enhancement respectively in the experimental verification.

Experiment 1:

defining the label of the negative sample as 1, namely, the negative sample is equal to the positive sample, but the position information is the first point at the upper left corner of the negative sample stitching IVOCT image, namely, the area containing vulnerable plaque in the whole image is only one point.

Experiment 2:

and defining the negative sample label as 0, and splicing the first point at the upper left corner of the IVOCT image by using the position information as the negative sample, wherein the positive sample and the negative sample are different labels.

Experiment 3:

defining the negative sample label as 0, and splicing the first point at the upper left corner of the IVOCT image by using the position information as the negative sample, namely, considering the positive sample and the negative sample as two completely different categories.

For comparison with subsequent data enhancement, no data enhancement was used in the above 3 experiments.

Table 1 is a table for evaluating the test results of experiments 1 to 3.

Experiment of	Recall rate R	Accuracy G	Degree of coincidence D	Finally detecting the quality factor S
					Experiment
1	85.56％	73.06％	78.69％	78.75％
					Experiment 2	86.17％	74.31％	79.51％	79.66％
Experiment 3	97.98％	66.21％	81.14％	80.08％

TABLE 1

According to the test result evaluation table in table 1, the test result of experiment 1 shows that the recall ratio is 85.56%, that is, there is a region with part of missed tests, the test result of experiment 2 shows that the recall ratio, accuracy and contact ratio are all improved compared with experiment 1, the test result of experiment 3 shows that the recall ratio and contact ratio are improved compared with the first two experiments, but the accuracy is reduced, and the final test quality factor of experiment 3 is optimal among 3 experiments. Therefore, the setting mode of the negative sample label can fully utilize the difference between the positive sample and the negative sample when the sample label is set, so that the performance of the final detection result is better.

Experiment 4:

on the basis of experiment 3, data enhancement was performed using the method in step S4. Table 2 is an evaluation table of the test results of experiment 4.

Experiment of	Recall rate R	Accuracy G	Degree of coincidence D	Finally detecting the quality factor S
					Experiment 4	94.42％	72.37％	83.33％	82.63％

TABLE 2

As can be seen from table 2, compared with the detection result of experiment 3, the accuracy in the detection result of experiment 4 is improved by 2.6% more than the recall rate, which is still about 94% even though it is reduced; the contact ratio of the laboratory 4 is improved to 83.33 percent, and the final detection quality factor is obviously improved.

According to the experiment, the IVOCT image vulnerable plaque automatic detection method based on pretreatment has good detection performance, the result is more objective, and the workload of doctors can be greatly reduced.

Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims

1. An IVOCT image vulnerable plaque automatic detection method based on preprocessing is characterized by comprising the following steps:

s3: denoising each sample IVOCT image under the polar coordinate;

2. The method for automatically detecting vulnerable plaque of IVOCT image of claim 1, wherein the specific method for denoising the sample IVOCT image in step S3 is as follows:

assigning zero values to all pixel values corresponding to the imaging catheter calibration ring and the imaging catheter, wherein the range of the imaging catheter area is set according to the actual situation; then, the upper edge of the blood vessel wall is detected, and the pixel values of the region above the upper edge of the blood vessel wall are set to zero.