Article... International Symposium on Medical Information Processing and Analysis. International SIPAIM Workshop2024
Tiny Lungs, Big Challenges: Pediatric and Premature Lung Segmentation using Deep Learning.
Article in ... International Symposium on Medical Information Processing and Analysis. International SIPAIM Workshop, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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