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A novel convolutional neural network, 3D-Res2UNet, combining 3D-UNet and Res2Net, achieves highly accurate lung nodule segmentation in CT images with a dice coefficient of 95.30% and a recall rate of 99.1%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 28, 2020Open Access

Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network

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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 strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.

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