Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome
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RESEARCH ARTICLE
Open Access
Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome Narathip Reamaroon1* , Michael W. Sjoding2 , Harm Derksen3 , Elyas Sabeti4 , Jonathan Gryak1 , Ryan P. Barbaro5 , Brian D. Athey1 and Kayvan Najarian1
Abstract Background: This study outlines an image processing algorithm for accurate and consistent lung segmentation in chest radiographs of critically ill adults and children typically obscured by medical equipment. In particular, this work focuses on applications in analysis of acute respiratory distress syndrome – a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year. Methods: Chest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the Sørensen–Dice coefficient to measure segmentation accuracy. Results: The TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 ±0.04 (min: 0.76) was reported for TVAC, 0.89 ±0.12 (min: 0.01) for U-Net, 0.74 ±0.19 (min: 0.15) for the random walker algorithm, and 0.64 ±0.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 ±0.04 (min: 0.75) was reported for TVAC, 0.87 ±0.09 (min: 0.56) for U-Net, 0.67 ±0.18 (min: 0.18) for the random walker algorithm, and 0.61 ±0.18 (min: 0.18) for the active spline model. Conclusion: The proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children. Keywords: Lung segmentation, Chest x-ray, Acute respiratory distress syndrome
Background Lung segmentation, the process of accurately identifying regions and boundaries of the lung field from surrounding thoracic tissue, is an essential first step in pulmonary image analysis of many clinical decision support systems. Correct identification of lung fields enables further com*Correspondence: [email protected] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA Full list of author information is available at the end of the article 1
putational analysis of these anatomical regions [1], such as extraction of clinically relevant features to train a machine learning algorithm for detection of disease and abnormalities. These computational methodologies can assist physicians with making a timely, accurate medical diagnosis to improve quality of care
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