CN110738658A - Image quality evaluation method - Google Patents
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Abstract
The invention relates to an image quality evaluation method for evaluating the quality of pathological images, which comprises the following steps: judging whether the pathological image is a digital pathological image obtained by scanning or a real-time acquired image obtained by a camera, when the pathological image is the digital pathological image, judging the imaging quality of the image, if the imaging quality is poor, ending the image quality evaluation, and outputting a judgment result; otherwise, judging the content quality of the image and outputting a judgment result; and when the pathological image is a real-time collected image, judging the content quality of the image and outputting a judgment result. The method and the device for judging the quality of the digital pathological image simulation medical expert firstly judge the scanning quality, directly define the pathological image with unqualified obvious scanning quality as an unqualified image, and do not judge the content quality, thereby improving the working efficiency of a quality control link.
Description
Technical Field
The invention relates to the field of image evaluation, in particular to an image quality evaluation method.
Background
The medical image is used for clinical diagnosis service and mostly adopts a subjective evaluation method, but the method needs repeated experiments by an organization doctor, is long in time consumption and high in cost, and meanwhile, the subjective evaluation method is easily influenced by the knowledge background, the observation purpose, the environment and the like of the doctor, has poor stability and portability, and is difficult to express by a mathematical model, so that can not be widely applied to .
In addition, the combination of artificial intelligence and pathological diagnosis, namely artificial intelligence auxiliary interpretation, is a new trend of improving the efficiency and accuracy of pathological diagnosis in the current medical field, and the quality of pathological images can influence the operation cost of a server and further influence the efficiency of auxiliary interpretation and the reliability of algorithm results, so that how to ensure the quality of input pathological images is also the technical problem which is mainly solved by artificial intelligence auxiliary interpretation.
Disclosure of Invention
aims to apply artificial intelligence to quality evaluation of pathological images so as to improve rapidity and accuracy of quality evaluation of the pathological images.
The invention realizes the purpose through the following technical scheme: an image quality evaluation method for evaluating the quality of a pathological image, comprising: judging whether the pathological image is a digital pathological image obtained by scanning or a real-time acquired image obtained by a camera, when the pathological image is the digital pathological image, judging the imaging quality of the image, if the imaging quality is poor, ending the image quality evaluation, and outputting a judgment result; otherwise, judging the content quality of the image and outputting a judgment result; and when the pathological image is a real-time collected image, judging the content quality of the image and outputting a judgment result.
, the method for judging the imaging quality of the digital pathological image specifically comprises the steps of sampling a th image of the digital pathological image, collecting a low-power image sample under m1 magnification, dividing the low-power image sample into a plurality of segmentation images with the same size, inputting the segmentation images with the same size into a low-power quality judgment model for judging the imaging quality, outputting the imaging quality score of each segmentation image by the low-power quality judgment model, judging the imaging quality of the low-power image sample according to the imaging quality score, and outputting a judgment result.
, the quality judgment method of the low power quality judgment model comprises taking the minimum value from the quality scores of each slice image, judging the digital pathological image as unqualified scanning quality if the minimum value is less than the threshold a, otherwise, judging the scanning quality as qualified.
, the method for judging the content quality of the digital pathological image includes sampling the digital pathological image for the second time, randomly collecting multiple high-power image samples with set sizes from m2 multiplying power, inputting the high-power image samples into a high-power quality judgment model for content quality judgment, and outputting the judgment result.
, the quality judgment method of the high power quality judgment model comprises the steps of inputting the multiple high power image samples into the high power quality judgment model, outputting the quality scores of each high power image sample by the high power quality judgment model, averaging the obtained quality scores, judging that the content quality is qualified if the obtained average value is less than a threshold b1, and otherwise, judging that the content quality is unqualified.
, the method for judging the content quality of the real-time collected image includes the steps of collecting the real-time collected image, inputting the collected real-time collected image into a high-power quality judgment model to judge the content quality, outputting the quality score of the image by the high-power quality judgment model, judging the real-time collected image to be qualified if the real-time collected image score is smaller than a threshold b3, and otherwise, judging the real-time collected image to be unqualified.
, the training process of the low power quality judgment model includes S10, collecting training data, obtaining a plurality of digital pathology whole field images, conducting low power sampling on the digital pathology whole field images to obtain digital pathology images with magnification of m1, adjusting the obtained digital pathology images to be the same size, dividing each digital pathology image into n1 sub-images with the same size, dividing all the sub-images into low power positive samples and low power negative samples according to imaging quality, and S11, training a deep learning model, establishing a convolutional neural network as a th slice quality judger, conducting training by adopting the low power positive samples and the low power negative samples to obtain the low power quality judgment model.
, the training process of the high power quality judgment model includes S20, collecting training data, obtaining a plurality of digital pathology whole field images, conducting high power sampling on the digital pathology whole field images to obtain digital pathology images with the magnification of m2, dividing each digital pathology image into n2 divided images with the same size, dividing all the divided images into high power positive samples and high power negative samples according to content quality, and S21, training the deep learning model, namely establishing a convolutional neural network as a second slice quality judger, conducting training by adopting the high power positive samples and the high power negative samples to obtain the high power quality judgment model.
Compared with the prior art, the invention has the following substantial effects: according to the method, the quality control link of the pathological images is placed before image processing, subsequent artificial intelligence auxiliary interpretation is not performed on the pathological images which are unqualified in evaluation, the operation cost of a server is reduced, and the efficiency of intelligent analysis and the reliability of algorithm results are improved; (2) for the judgment process of the digital pathological image simulation medical expert, the scanning quality is judged firstly, the pathological image with unqualified obvious scanning quality is directly defined as the unqualified image, and the content quality is not judged, so that the working efficiency of a quality control link is improved.
Drawings
Fig. 1 is a flow chart of image quality evaluation methods of the present invention.
Detailed Description
The invention is further described with reference to the following drawings:
in the embodiment, the pathological image is obtained by scanning pathological slides by a scanner to obtain a high-resolution digital pathological image, and the is obtained by collecting pathological sections under a microscope in real time through a microscope camera arranged on the microscope.
The image quality evaluation method is used for evaluating the image quality of pathological images, for digital pathological images, the image quality comprises the imaging quality and the content quality, the imaging quality comprises the defects of whether pathological slide scanning is distorted, whether a large amount of stains, dust, bubbles, water drops and the like exist on a section, whether an effective tissue area is contained, and the content quality comprises the defects of whether cell cavities exist in the section, few cells, much blood and the like. Under the low power visual field, the range of the observed digital pathological image is large, which is beneficial to judging the image quality on the whole, but the details of the cell level can not be observed, so that the digital pathological image with qualified imaging quality needs to be converted into the high power visual field so as to judge the content quality of the pathological image more pertinently.
As shown in figure 1, after a pathological image is received, whether the pathological image is a digital pathological image or a real-time acquired image is judged, if the pathological image is the digital pathological image, the imaging quality of the image is judged firstly, th image sampling is carried out on the digital pathological image, a low-power image sample under the magnification of m1 (for example under the magnification of 1.25) is acquired, the low-power image sample is divided into a plurality of divided images with the same size, the plurality of divided images with the same size are input into a low-power quality judgment model to be subjected to imaging quality judgment, the low-power quality judgment model outputs the imaging quality score of each divided image, the quality score of each divided image is taken as the minimum value, if the minimum value is smaller than a threshold value a, the digital pathological image is judged to be unqualified in scanning quality, imaging quality evaluation is finished, a judgment result is output, otherwise, the scanning quality is qualified, steps are carried out on the digital pathological image, a plurality of high-power image samples with set sizes are randomly acquired under the magnification of m2, the content of the high-power image is input into a high-power image, otherwise, the content of the high-power image is judged to be smaller than an average value, the content of the high-power image, the content of the high-quality judgment model is selected, and the content of the quality score is selected according to be judged to be not to be judged to be smaller than an experimental quality score, otherwise, the average value of the same.
For the pathological section directly observed under the microscope, the picture of the pathological section under the microscope lens can be collected through the microscope camera arranged under the microscope, the collected pathological image is called as a real-time collected image in the application, and in consideration of the practical work, when a doctor observes the pathological section under the microscope, the microscope is usually adjusted to be suitable for the observed high-power visual field, and the collected real-time collected image is adapted to the size of the training sample of the high-power quality judgment model. Therefore, for the real-time collected image, the acquired picture of the pathological section under the current microscope visual field under the high-power visual field is acquired by default, namely the acquired pathological image under the high-power visual field is acquired, the acquired real-time collected image is input into the high-power quality judgment model for content quality judgment, the high-power quality judgment model outputs the quality score of the real-time collected image, and if the quality score of the real-time collected image is smaller than a threshold value b3, the real-time collected image is judged to be qualified in content quality; otherwise, the content quality is unqualified. The threshold b3 is selected according to experiment and the required scene of quality level.
When carrying out the low power quality judgment model and the high power quality judgment model training, the method comprises the following steps:
1. training data was collected, the training data set containing about thousand full-field slices of good or poor quality digital pathology.
a) And performing low-power sampling on all digital full-field slices to obtain a full-field image with the magnification of 1.25 (the side length of the -like image is between 2000 and 3000 pixels), then scaling the obtained full-field image to 2048 pixels by 2048 pixels, then cutting the full-field image into a plurality of 1024 pixels by 1024 segmented images, finally dividing all segmented images into a low-power positive sample and a low-power negative sample according to the imaging quality of the segmented images, using 1 to represent the positive sample, and using 0 to represent the negative sample.
b) High-power sampling is carried out on all digital full-field slices to obtain full-field images with the magnification of 20 times (the side length of -shaped images is between 30000-50000 pixels), each full-field image is segmented into a plurality of segmented images with the size of 1024 x 1024 pixels, finally the segmented images are divided into high-power positive samples and high-power negative samples according to the content quality, 1 represents the positive samples, and 0 represents the negative samples.
2. The deep learning model is trained by establishing two Convolutional Neural Networks (CNN) of a structure sample as slice quality judgers, respectively training the convolutional neural networks by using high-power sampled training data and low-power sampled training data, wherein the obtained models are a high-power quality judgment model and a low-power quality judgment model, the model consists of a convolutional layer, a pooling layer, an activation function, a BN (BatchNormalization) layer, a full connection layer and a jump connection, the convolutional layer is used for coding the characteristics of an upper layer, the activation function generates nonlinear transformation, the pooling layer is used for reducing the dimension of a characteristic diagram so as to reduce the parameter quantity of the network and accelerate the training and reasoning of the model, the full connection layer is used for integrating and classifying the obtained characteristics, the jump connection is used for directly transmitting the previous characteristic diagram into a deeper network structure through a shortcut path without passing through a main path, the layers and the functions are organized through a definite design structure, the neural network structure is formed, the better the input of the neural network is 1024 pixels, and the 1024 images output scores which are closer to 1, and the scores are closer to 5390 and the higher the quality of the images.
The slice quality judging method simulates the process of human expert judgment, and for a digital pathological image, whether slice scanning is distorted or not is roughly judged from a low-power visual field, whether a large amount of stains, dust, bubbles and water drops exist on the slice or not, whether the slice contains an effective tissue area or not is judged, if the slice does not contain the problems, steps are carried out for sampling judgment from a high-power visual field, because details at a cell level, such as cell cavities, few cells, blood and the like, cannot be observed in the low-power visual field, the efficiency and the accuracy of pathological image quality judgment are improved through step-by-step judgment of scanning quality and content quality, and preparation is made for artificial intelligent auxiliary judgment.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (8)
1. An image quality evaluation method for evaluating quality of a pathological image, comprising:
judging whether the pathological image is a digital pathological image obtained by scanning or a real-time acquired image obtained by a camera, when the pathological image is the digital pathological image, judging the imaging quality of the image, if the imaging quality is poor, ending the image quality evaluation, and outputting a judgment result;
otherwise, judging the content quality of the image and outputting a judgment result;
and when the pathological image is a real-time collected image, judging the content quality of the image and outputting a judgment result.
2. The method for evaluating the image quality according to claim 1, wherein the method for judging the imaging quality of the digital pathological image comprises the steps of sampling the th image of the digital pathological image, collecting a low-power image sample at a magnification of m1, dividing the low-power image sample into a plurality of divided images with the same size, inputting the plurality of divided images with the same size into a low-power quality judgment model for judging the imaging quality, outputting the imaging quality score of each divided image by the low-power quality judgment model, judging the imaging quality of the low-power image sample according to the imaging quality score, and outputting the judgment result.
3. The image quality evaluation method according to claim 2, wherein the method of performing quality judgment by the low power quality judgment model includes:
taking the minimum value from the quality scores of each slice image, and judging the digital pathological image as unqualified scanning quality if the minimum value is less than a threshold value a; otherwise, the scanning quality is qualified.
4. The image quality evaluation method according to claim 1, wherein the method for determining the content quality of the digital pathology image specifically includes: and carrying out second image sampling on the digital pathological image, randomly acquiring a plurality of high-power image samples with set sizes from m2 multiplying power, inputting the high-power image samples into a high-power quality judgment model for content quality judgment, and outputting a judgment result.
5. The image quality evaluation method according to claim 4, wherein the method of performing quality judgment by the high power quality judgment model includes:
inputting the multiple high-power image samples into a high-power quality judgment model, outputting the quality score of each high-power image sample by the high-power quality judgment model, averaging the obtained quality scores, and judging the digital pathological image to be qualified in content quality if the obtained average value is less than a threshold value b 1; otherwise, the content quality is unqualified.
6. The image quality evaluation method according to claim 1, wherein the method for determining the content quality of the real-time captured image specifically comprises: acquiring images of the real-time acquired images, inputting the acquired real-time acquired images into a high-power quality judgment model for content quality judgment, outputting quality scores of the images by the high-power quality judgment model, and judging the real-time acquired images to be qualified in content quality if the scores of the real-time acquired images are smaller than a threshold value b 3; otherwise, the content quality is unqualified.
7. The image quality evaluation method according to claim 3, wherein the training process of the low power quality determination model includes:
s10, training data are collected:
acquiring a plurality of digital pathology full-field pictures, performing low-power sampling on the digital pathology full-field pictures to acquire digital pathology images with the magnification of m1, adjusting the acquired digital pathology images to be the same size, cutting each digital pathology image into n1 sub-images with the same size, and dividing all the sub-images into low-power positive samples and low-power negative samples according to the imaging quality;
s11, training a deep learning model:
and establishing a convolutional neural network as an th slice quality judger, and training by adopting a low-power positive sample and a low-power negative sample to obtain a low-power quality judgment model.
8. The image quality evaluation method according to claim 4, 5 or 6, wherein the training process of the high power quality determination model includes:
s20, training data are collected:
acquiring a plurality of digital pathology whole-field images, performing high-power sampling on the digital pathology whole-field images to obtain digital pathology images with the magnification of m2, dividing each digital pathology image into n2 divided images with the same size, and dividing all the divided images into high-power positive samples and high-power negative samples according to the content quality;
s21, training a deep learning model:
and establishing a convolutional neural network as a second slice quality judger, and training by adopting a high-power positive sample and a high-power negative sample to obtain a high-power quality judgment model.
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