An automatic trough line identification method based on improved UNet

Cai, Yali; Li, Qian; Yin, Fan; Zhang, Liang; Huang, Hong; Ding, Xiuli

doi:10.1016/j.atmosres.2021.105839

Cited by 4 publications

(1 citation statement)

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“…The loss function optimizes the network structure by backpropagating the numerical error of the calculated loss function and continuously updating the weights. In the field of medical image segmentation, the Dice Loss [ 45 ] function is commonly used to calculate the degree of differences between the predicted region and the real region. The concepts of Dice Loss are defined by ( 3 ).…”

Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net

Wang

Jiang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients’ rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.

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Section: Methodsmentioning

confidence: 99%