Artificial intelligence medical image quality control method applied to clinical image

A medical imaging and artificial intelligence technology, applied in the field of artificial intelligence medical imaging quality control, can solve problems such as misjudgment, inaccuracy, and heavy workload, and achieve the goal of reducing time cost, reducing image quality, and avoiding subjective bias Effect

Pending Publication Date: 2021-10-26
JIANGSU GFOUND INFORMATION TECH CO LTD
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The application number is CN109741317A, which discloses a medical image intelligent evaluation method, which realizes automatic intelligent judgment of medical image quality with the help of multiple convolutional neural network models, adopts the U-Net model, but after adopting U-Net segmentation, due to the Often the color is darker. During the neural network prediction process, sometimes the surrounding dark area is mistaken for the lung field area, so there will be white noise blocks outside the lung field area; similarly, there will occasionally be in the actual lung field area. Black noise, as if there

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  • Artificial intelligence medical image quality control method applied to clinical image
  • Artificial intelligence medical image quality control method applied to clinical image
  • Artificial intelligence medical image quality control method applied to clinical image

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Experimental program
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Effect test

Embodiment 1

[0052] Such as figure 1 As shown, an artificial intelligence medical image quality control method applied to clinical images includes: the technician collects patient images, and transmits the images to the artificial intelligence medical image quality control management system, and the artificial intelligence medical image quality control management system is used to control Semantic segmentation, classification processing and quality control scoring are performed on the image, and the score is displayed on the operation interface where the technician previews the image. The technician judges whether it is necessary to remark or re-acquire the image according to the quality control score and the patient's condition, so as to reduce the occurrence of low-score image quality , using the front-end quality control method, which greatly reduces the time cost of technicians reviewing films, avoids subjective bias, realizes accurate and effective quality control methods for the entir...

Embodiment 2

[0061] Compared with Embodiment 1, the establishment method of the image semantic segmentation model in step S1 of this embodiment is as follows:

[0062] From the patient's chest radiograph database, several images were selected as the training set and test set, and the labelme software was used to complete the labeling of the polygonal points of the left and right lung fields, scapula and clavicle regions of the selected images, and the labeling The result is saved (saved in json format), and then the python script is used for batch processing to generate a binarized mask image of each area, in which the corresponding area is white, the corresponding gray value is 1, the background area is black, and the corresponding gray The degree value is 0, and the original chest film used for labelme is as follows figure 2 As shown, the mask images of the lung field, scapula and clavicle region generated after labelme labeling are as follows Figure 3-Figure 5 shown.

[0063] In thi...

Embodiment 3

[0074] Compared with Embodiment 2, the calculation method of each data in step S3 of this embodiment and the corresponding quality control standard scoring rules are as follows:

[0075] The formula for calculating body position offset is as follows:

[0076] Δx=|x p -x im |

[0077] Among them, Δx is the body position offset, x im The coordinates of the center point of the picture; x p is the coordinates of the center point of the patient's body position, and its calculation formula is x 1 is the maximum value of the point coordinates in the left clavicle connected domain, x 2 is the minimum value of the point coordinates in the right clavicular connected domain;

[0078] When Δx>50, it is considered that there is a body position deviation, and 1 point will be deducted.

[0079] The calculation method of the shoulder shrug is as follows: find the circumscribed rectangle for the left and right clavicles in the figure obtained in step S1, and the horizontal angle betwe...

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Abstract

The invention provides an artificial intelligence medical image quality control method applied to a clinical image, a technician collects a patient image and transmits the image to an artificial intelligence medical image quality control management system, and the artificial intelligence medical image quality control management system is used for performing semantic segmentation, classification processing and quality control scoring on the image. The score is displayed on an operation interface of a technician previewing image, and the technician judges whether to remark or recollect the image according to the quality control score and the condition of a patient, so that the generation of low-score image quality is reduced, the limitations of low efficiency, large workload, small quantity of quality control samples and strong subjectivity of traditional quality control are solved, accurate and effective quality control means are applied to the whole process from image data acquisition to diagnosis and are combined with primary technician and physician team management, and a standardized and intelligent image quality control system is established.

Description

technical field [0001] The invention relates to the technical field of medical image quality control, in particular to an artificial intelligence medical image quality control method applied to clinical images. Background technique [0002] DR chest film, that is, Radio Radiography digital X-ray image, has become the best clinical diagnosis for the initial screening of chest and lung diseases in recent years because of its extremely low radiation dose, low cost of equipment, high density resolution, and fast imaging. Way. With the increase of patients and clinical needs and the trend of digital and intelligent transformation, the intelligent quality control of DR chest radiographs is particularly important. [0003] Focusing on the discussion and research on the future of quality control in imaging departments across the country, most of them discuss and formulate a series of solutions for the unity of quality control standards, consistency of norms, and objectivity of manu...

Claims

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Application Information

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IPC IPC(8): G16H30/20G16H30/40G06T7/10G06N3/04G06N3/08
CPCG16H30/20G16H30/40G06T7/10G06N3/08G06T2207/20081G06T2207/20084G06T2207/30061G06T2207/30008G06N3/045
Inventor 连泽宇胡安宁
Owner JIANGSU GFOUND INFORMATION TECH CO LTD
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