Multi-center child X-ray chest radiography image lung segmentation method based on TransUNet model
A multi-center, children's technology, applied in the field of medical artificial intelligence, can solve the problems that the accuracy and efficiency of segmentation need to be further improved, and the long-range relationship cannot be effectively modeled, so as to achieve good generalization, improvement of segmentation accuracy and efficiency, and good segmentation performance effect
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[0038] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be pointed out that the following embodiments are intended to facilitate the understanding of the present invention, but do not have any limiting effect on it.
[0039] like figure 1 As shown, a multi-center child X-ray chest X-ray image lung segmentation method based on the TransUNet model includes the following steps:
[0040] 1. Image preprocessing
[0041] Collect children's chest X-ray image data from multiple centers. Since the images come from multiple centers, the imaging standards are inconsistent, so grayscale truncation of the images is required. In addition, chest X-rays are skewed, and the chest is not completely centered on the image due to the size of the doctor's crop. Therefore, the steps of grayscale truncation are as follows: first obtain the image size (x, y), and then intercept the central area [x / 4:x*3 / 4,y / 4:y*3 / ...
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