Automated semantic lung segmentation in chest CT images using deep neural network

Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed
tomography (CT) images with DeepLabV3 ? networks for two-class (background and lung field) and four-class (groundglass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the
DeepLabV3 ? network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and
ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixellabeled images are used to develop the deep learning model. The segmentation performance has been assessed using five
performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 ? network with ResNet-18 and a batch size of 8
have a higher performance for two-class segmentation. DeepLabV3 ? network coupled with ResNet-50 and a batch size of
16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a
fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 ? network with Xception. This present work proposes a unified DeepLabV3 ? network to delineate the two and four different regions automatically using CT images for CoVID19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis.