  3D lung segmentation is essential since it processes the volumetric
information of the lungs, removes the unnecessary areas of the scan, and
segments the actual area of the lungs in a 3D volume. Recently, the deep
learning model, such as U-Net outperforms other network architectures for
biomedical image segmentation. In this paper, we propose a novel model, namely,
Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In
particular, the proposed model integrates 3D convolution into the Recurrent
Residual Neural Network based on U-Net. It helps learn spatial dependencies in
3D and increases the propagation of 3D volumetric information. The proposed
R2U3D network is trained on the publicly available dataset LUNA16 and it
achieves state-of-the-art performance on both LUNA16 (testing set) and VESSEL12
dataset. In addition, we show that training the R2U3D model with a smaller
number of CT scans, i.e., 100 scans, without applying data augmentation
achieves an outstanding result in terms of Soft Dice Similarity Coefficient
(Soft-DSC) of 0.9920.
