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A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images

Khanna Anita, Londhe Narendra D., Gupta S., Semwal Ashish

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1314--1327

To improve the early diagnosis and treatment of lung diseases automated lung segmentation from CT images is a crucial task for clinical decision. The segmentation of the lung region from the CT scans is a very challenging task due to the irregular shape and size of lungs, low contrast and fuzzy boundaries of the lung. The manual segmentation of lung CT images is a laborious task. Therefore, various approaches are suggested by the researcher in the recent past for the automated lung segmentation. However, the existing approaches either utilize low-level handcraft features or CNN based Fully Convolutional Networks. The low-level hand-craft feature-based approaches lead to poor generalization, while the shallower networks are unable to extract more discriminative features. Hence, in this study, we have implemented a deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation. Here, we have suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation. To take full advantage of the deeper network, we have utilized a set of schemes to ensure efficient training. First, we have implemented a U-Net architecture with residual block to overcome the problem of performance degradation. Further, various data augmentation techniques are utilized to improve the generalization capability of the proposed method. The experimental results show that the proposed method achieved competitive results over the existing methods with DSC of 98.63%, 99.62% and 98.68% for LUNA16, VESSEL12 and HUG-ILD dataset respectively.