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Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.
Cheung, Wing Keung; Pakzad, Ashkan; Mogulkoc, Nesrin; Needleman, Sarah Helen; Rangelov, Bojidar; Gudmundsson, Eyjolfur; Zhao, An; Abbas, Mariam; McLaverty, Davina; Asimakopoulos, Dimitrios; Chapman, Robert; Savas, Recep; Janes, Sam M; Hu, Yipeng; Alexander, Daniel C; Hurst, John R; Jacob, Joseph.
Afiliação
  • Cheung WK; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • Pakzad A; Department of Computer Science, University College London, London, UK.
  • Mogulkoc N; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • Needleman SH; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
  • Rangelov B; Department of Respiratory Medicine, Ege University Hospital, Izmir, Turkey.
  • Gudmundsson E; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • Zhao A; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
  • Abbas M; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • McLaverty D; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
  • Asimakopoulos D; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • Chapman R; Department of Computer Science, University College London, London, UK.
  • Savas R; Satsuma Lab, Centre for Medical Image Computing, University College London, 1st Floor, 90 High Holborn, London, WC1V6LJ UK.
  • Janes SM; Department of Computer Science, University College London, London, UK.
  • Hu Y; Department of Computer Science, University College London, London, UK.
  • Alexander DC; Medical School, University College London, London, UK.
  • Hurst JR; School of Clinical Medicine, University of Cambridge, Cambridge, UK.
  • Jacob J; Interstitial Lung Disease Service, Department of Respiratory Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
J Big Data ; 11(1): 104, 2024.
Article em En | MEDLINE | ID: mdl-39109339
ABSTRACT
The morphology and distribution of airway tree abnormalities enable 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. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of 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 segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article