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3D lung segmentation is essential since it processes the volumetricinformation of the lungs, removes the unnecessary areas of the scan, andsegments the actual area of the lungs in a 3D volume. Recently, the deeplearning model, such as U-Net outperforms other network architectures forbiomedical image segmentation. In this paper, we propose a novel model, namely,Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. Inparticular, the proposed model integrates 3D convolution into the RecurrentResidual Neural Network based on U-Net. It helps learn spatial dependencies in3D and increases the propagation of 3D volumetric information. The proposedR2U3D network is trained on the publicly available dataset LUNA16 and itachieves state-of-the-art performance on both LUNA16 (testing set) and VESSEL12dataset. In addition, we show that training the R2U3D model with a smallernumber of CT scans, i.e., 100 scans, without applying data augmentationachieves an outstanding result in terms of Soft Dice Similarity Coefficient(Soft-DSC) of 0.9920.

R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation