Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet

Abstract

Lung segmentation using computed tomography (CT) images is important for diagnosing various lung diseases. Currently, no lung segmentation method has been developed for assessing the CT images of preschool children, which may differ from those of adults due to (1) presence of artifacts caused by the shaking of children, (2) loss of a localized lung area due to a failure to hold their breath, and (3) a smaller CT chest area, compared with adults. To solve these unique problems, this study developed an automatic lung segmentation method by combining traditional imaging methods with ResUnet using the CT images of 60 children, aged 0-6 years. First, the CT images were cropped and zoomed through ecological operations to concentrate the segmentation task on the chest area. Then, a ResUnet model was used to improve the loss for lung segmentation, and case-based connected domain operations were performed to filter the segmentation results and improve segmentation accuracy. The proposed method demonstrated promising segmentation results on a test set of 12 cases, with average accuracy, Dice, precision, and recall of 0.9479, 0.9678, 0.9711, and 0.9715, respectively, which achieved the best performance relative to the other six models. This study shows that the proposed method can achieve good segmentation results in CT of preschool children, laying a good foundation for the diagnosis of children’s lung diseases.

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

“…The core idea of SE (squeeze and excitation) attention mechanism is to ignore irrelevant information and only extract regions of interest, which includes three operations: sequence, exclusion, and scale. Sequence uses a global average pooling to solve the mean value of all elements on each channel, that is, map the input feature map to the same low-dimensional space as the number of feature channels 30 . Excitation is the core operation of SE.…”

“…In view of the shortcomings of Unet in performance, BN layer is introduced to eliminate the covariate shift within the network, improve the generalization and convergence speed, and the Residual-module is introduced to enhance the edge thinning 29 . Li first uses Gaussian filter to de-noise the image, then processes the CT image through ecological operation, and finally uses Res Unet model to improve the segmentation loss and improve the segmentation accuracy and speed 30 . Lin Yuchun et al studied the performance of U-Net in fully automatic positioning and segmentation in magnetic resonance (MR) images, and proved that Unet network can perform more accurate segmentation in diffusion-weighted MR images 31 .…”

Optimization of Median Modified Wiener Filter for Improving Lung Segmentation Performance in Low-Dose Computed Tomography Images

“…Sci. 2023, 13, 10679 2 of 13 lung [5]. Furthermore, lung segmentation is essential for analyzing lung parenchymal density and quantitatively analyzing mechanisms related to the lung and diaphragm regions [6].…”