Multi-center child X-ray chest radiography image lung segmentation method based on TransUNet model

Abstract

The invention discloses a multi-center child X-ray chest radiography image lung segmentation method based on a TransUNet model. The method comprises the following steps: (1) collecting a multi-center child X-ray chest radiography image and preprocessing the multi-center child X-ray chest radiography image; (2) dividing the data into a training set, a verification set and a test set; (3) a segmentation model is constructed, a Transform layer is added to the segmentation model on the basis of UNet, and the segmentation model comprises four parts of three-time down-sampling, a linear layer, the Transform layer and up-sampling; (4) sending the training set into the constructed segmentation model for training, evaluating the performance of the segmentation model by using the verification set, adjusting the hyper-parameters of the model according to the evaluation effect, and finally obtaining the segmentation model with the performance reaching the standard through repeated training and verification; and (5) inputting a to-be-segmented multi-center child X-ray chest radiography image into the trained segmentation model so as to intelligently segment a lung region. The method provided by the invention combines the advantages of the Transformers network and the UNet network, and has relatively high segmentation precision and efficiency.

Description

technical field [0001] The invention belongs to the field of medical artificial intelligence, in particular to a lung segmentation method based on a TransUNet model of a multi-center children's X-ray chest X-ray image. Background technique [0002] Pneumonia in children is the top three diseases in children with infection rates. At present, many researchers use intelligent algorithms to diagnose pneumonia in children, and the pre-step of image-based diagnosis is the segmentation of lungs. Image-based lung classification also helps provide accuracy in lung nodule detection, bronchitis detection, and more. Conventional lung detection images for children mainly include CT and chest X-ray. The former has many slices and generally has higher segmentation accuracy, while the latter has only one image, and the quality of the images obtained on different machines is different, so lung segmentation is performed on them. more difficult. [0003] In response to this problem, many res...

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Problems solved by technology

[0006] However, due to the inherent locality of convolution in these existing networks, convolution-based methods cannot

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