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arXiv · 2308.00008

Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance

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
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Abstract

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.

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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. 2024-07-23. Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance. https://doi.org/10.1186/s40537-024-00974-x

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