Efficient Lung Segmentation from Chest Radiographs using Transfer Learning and Lightweight Deep Architecture

Lung delineation constitutes a critical preprocessing stage for X-ray-based diagnosis and follow-up. However, automatic lung segmentation from chest radiographs (CXR) poses a challenging problem due to anatomical structures' varying shapes and sizes, the differences between radio-opacity, contrast, and image quality, and the requirement of complex models for automatic detection of regions of interest. This work proposes the automated lung segmentation methodology DenseCX, based on U-Net architectures and transfer learning techniques. Unlike other U-Net networks, DenseCX includes an encoder built from Dense blocks, promoting a meaningful feature extraction with lightweight layers. Then, a homogeneous domain adaptation transfers the knowledge from classifying a large cohort of CXR to the DenseCX, reducing the overfitting risk due to the lack of manually labeled images. The experimental setup evaluates the proposed methodology on three public datasets, namely Shenzhen Hospital Chest X-ray, the Japan Society of Radiological Technology, and Montgomery County Chest X-ray, in a leave-one-group-out validation strategy for warranting the generalization. The attained Dice, Sensitivity, and Specificity metrics evidence that DenseCX outperforms other conventional ImageNet initialization while providing the best trade-off between performance and model complexity than state-of-the-art approaches, with a much lighter architecture and an improved convergence.