A data-centric deep learning approach to airway segmentation
Wing Keung Cheung Ashkan Pakzad Nesrin Mogulkoc Sarah Needleman Bojidar Rangelov Eyjolfur Gudmundsson An Zhao Mariam Abbas Davina McLaverty Dimitrios Asimakopoulos Robert Chapman Recep Savas Sam M Janes Yipeng Hu Daniel C. Alexander John R Hurst Joseph Jacob
The morphology and distribution of airway tree abnormalities enables
diagnosis and disease characterisation across a variety of chronic respiratory
conditions. In this regard, airway segmentation plays a critical role in the
production of the outline of the entire airway tree to enable estimation of
disease extent and severity. In this study, we propose a data-centric deep
learning technique to segment the airway tree. The proposed technique utilises
interpolation and image split to improve data usefulness and quality. Then, an
ensemble learning strategy is implemented to aggregate the segmented airway
trees at different scales. In terms of segmentation performance (dice
similarity coefficient), our method outperforms the baseline model by 2.5% on
average when a combined loss is used. Further, our proposed technique has a low
GPU usage and high flexibility enabling it to be deployed on any 2D deep
learning model.