Tiny Lungs, Big Challenges: Pediatric and Premature Lung Segmentation using Deep Learning

This is the authors' abstract. We don't add key points for this paper.

Lung segmentation in x-rays is a critical step for automated clinical diagnosis and severity grading of various pulmonary diseases. Lung segmentation from premature chest x-rays is particularly challenging due to tiny size of lungs, variability of anatomical presentations, and presence of radiological artifacts. We propose a two-step deep learning based method for lung delineation: we first perform lung detection, which is followed by segmentation of the retro-cardiac lung region. We finetune a segmentation model (UNETR) using pediatric (407 images) and premature (193 images) cohorts with a weighted loss. The model is first pretrained on a large chest x-ray data (∼31,000 scans). Our proposed strategy accurately segments lungs in both pediatric and premature x-rays with a mean Dice score of 0.960 and 0.946, and a mean Hausdorff distance of 6.576 and 8.124 pixels respectively.