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15 March 2019 A fully automated CT-based airway segmentation algorithm using deep learning and topological leakage detection and branch augmentation approaches
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Abstract
Quantitative CT-based characterization of bronchial morphology is widely used in chronic obstructive pulmonary disease (COPD) related research and clinical studies. There are no fully automated airway tree segmentation methods, which is critical for large multi-site COPD studies. A critical challenge is that airway segmentation failures, e.g., leakages or early truncation, in even a small fraction of cases warrants manual intervention for all cases. In this paper, we present a fullyautomated CT-based hybrid algorithm for human airway segmentation that combines both deep learning and conventional image processing approaches. A three-dimensional (3-D) U-Net is developed to compute a voxel-level likelihood map of airway lumen space from a chest CT image at total lung capacity (TLC). This likelihood map is fed into a conventional image processing cascade that iteratively augments airway branches and removes leakages using newly developed freezeand-grow and progressive threshold parameter relaxation approaches. The new method has been applied on fifteen TLC human chest CT scans from an ongoing COPD Study and its performance has been quantitatively compared with the results of a semi-automated industry-standard software involving manual review and correction. Experimental results show significant improvements in terms of branch level accuracy using the new method as compared to the unedited results from the industry-standard method, while matching with their manually edited results. In terms of segmentation volume leakage, the new method significantly reduced segmentation leakages as compared to both unedited and edited results of the industry-standard method.
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Syed Ahmed Nadeem, Eric A. Hoffman, and Punam K. Saha "A fully automated CT-based airway segmentation algorithm using deep learning and topological leakage detection and branch augmentation approaches", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490C (15 March 2019); https://doi.org/10.1117/12.2512286
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