EfficientUNet: Modified encoder-decoder architecture for the lung segmentation in chest x-ray images

Chest x-rays are a widely used medical imaging technique for the early detection of pulmonary diseases. However, the increasing workload and shortage of radiologists have motivated the research community to explore the possibilities in automated diagnosis. Segmentation is the most critical and essential procedure to improve the automatic diagnosis. Irregular lung shape and size with the overlapped lung regions make the identification of lung boundaries difficult. Further, the dense abnormalities often have high-intensity values that can be interpreted as lung boundaries. UNet is the most popular segmentation architecture among the deep learning community. Despite its high performance, there is scope for improvement. Most of the proposed segmentation models for lung segmentation in chest x-rays are trained and tested on a same dataset. It is reported that the model trained on one dataset fails when tested on another dataset. The aim of this study is to develop an efficient architecture for accurate lung segmentation in chest x-rays. The proposed model uses the pre-trained encoder, and the decoder uses residual learning and batch normalization to improve the performance. The publicly available Shenzhen, Montgomery County, and the NIH datasets are used for the evaluation of the proposed model. It is also validated by cross-dataset generalization. The proposed model obtained the 96.14% DSC and 92.67% JI value on the Shenzhen dataset, while 98.48% DSC and 97.01% JI value on the MC dataset. For cross-dataset evaluation, the EfficientUNet reported the DSC value of 95.97% and JI value of 92.34% for the MC dataset. While the DSC value of 94.02% and JI value of 88.86% on the NIH dataset. The obtained results across the different metrics show the efficiency of the proposed model. Unlike, the other state-of-the-art models, the proposed model is evaluated on the lungs that are highly irregular in shape and size due to different pulmonary abnormalities.

Automated summary

Chest x-rays are widely used for early detection of pulmonary diseases, but the increasing workload and shortage of radiologists have prompted researchers to explore automated diagnosis. Segmentation is critical for improving automatic diagnosis, but identifying lung boundaries is difficult due to irregular shape and size and overlapping lung regions. UNet is the most popular segmentation architecture, but there is room for improvement. Most proposed models for lung segmentation in chest x-rays are trained and tested on the same dataset, resulting in failure when tested on another dataset. This study aims to develop an efficient architecture for accurate lung segmentation using pre-trained encoders, residual learning, and batch normalization.