2020
DOI: 10.3390/sym12111787
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Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network

Abstract: Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with… Show more

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Cited by 71 publications
(28 citation statements)
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References 18 publications
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“…For this reason, two of the works brought up in Section 1 were chosen. Cao et al 2019 (DBResNet) [ 29 ] and Xiao et al 2020 (3D-UNet) [ 32 ] both proposed lung nodule segmentation methods which obtained competitive results. Their proposals have been evaluated on the public LIDC dataset, and produced Dice scores of 82.74% and 95.30% respectively.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…For this reason, two of the works brought up in Section 1 were chosen. Cao et al 2019 (DBResNet) [ 29 ] and Xiao et al 2020 (3D-UNet) [ 32 ] both proposed lung nodule segmentation methods which obtained competitive results. Their proposals have been evaluated on the public LIDC dataset, and produced Dice scores of 82.74% and 95.30% respectively.…”
Section: Resultsmentioning
confidence: 99%
“…In most recent works the U-Net architecture has been popular with three-dimensional implementations that improve its performance. Funke et al [ 31 ] trained a 3D-UNet model using using the STAPLE algorithm, and Xiao et al [ 32 ] combined the 3D-UNet and Res2Net architectures to create a new model which reached a Dice score of 95.3% on the Lung Image Database Consortium (LIDC) dataset. In a different approach, Hu et al [ 33 ] utilized a hybrid attention mechanism and densely connected convolutional networks, reaching a Dice score of 94.6%.…”
Section: Introductionmentioning
confidence: 99%
“…Lung segmentation is a necessary and critical step for the diagnosis and treatment of lung diseases, especially in the early stage. Conventionally, U-net, a symmetric model architecture that is widely used in medical image segmentation, is applied for lung [20] and lung lesion/nodule segmentation [21] . However, this method requires lung delineation masks that are paired to each input CT slice for training.…”
Section: Related Workmentioning
confidence: 99%
“…In order to solve the problem of inaccurate positioning caused by insufficient information extraction in 2DCNN, we increase the temporal and spatial information by introducing a 3D convolution kernel The results Article show the comparison between the positioning prediction effect of 3DCNN and 2DCNN. In order to increase the credibility of the results, we also added four additional sets of comparative experiments, including three classic models, AlexNet [30],Vgg16 [31],Vgg19 [32], which have 2d convolution kernel structure and 3D-Unet [33] that also has 3d convolution kernel structure. The result shows that our proposed 3DCNN model on task of locating with improvements of 20%, 12%,12%,9%,7% on 20pixels than 2DCNN,AlexNet,Vgg16,Vgg19,3D-Unet, respectively.…”
Section: The Effection Of Locationmentioning
confidence: 99%