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

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dc.authoridAlexander, Daniel/0000-0003-2439-350X
dc.authoridHurst, John/0000-0002-7246-6040
dc.authoridPakzad, Ashkan/0000-0002-0802-5590
dc.contributor.authorCheung, Wing Keung
dc.contributor.authorPakzad, Ashkan
dc.contributor.authorMogulkoc, Nesrin
dc.contributor.authorNeedleman, Sarah Helen
dc.contributor.authorRangelov, Bojidar
dc.contributor.authorGudmundsson, Eyjolfur
dc.contributor.authorZhao, An
dc.date.accessioned2024-08-31T07:50:52Z
dc.date.available2024-08-31T07:50:52Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractThe 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.en_US
dc.description.sponsorshipRosetrees Award [209553/Z/17/Z]; Wellcome Trust Clinical Research Career Development Fellowship [227835/Z/23/Z]; Wellcome Trust Career Development Fellowship; NIHR Biomedical Research Centre at University College London [209553/Z/17/Z]; Wellcome Trust; Lung CT Analyzer projecten_US
dc.description.sponsorshipJJ was supported by Wellcome Trust Clinical Research Career Development Fellowship 209553/Z/17/Z, Wellcome Trust Career Development Fellowship 227835/Z/23/Z, and the NIHR Biomedical Research Centre at University College London. This research was funded in whole or in part by the Wellcome Trust [209553/Z/17/Z]. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission. The airway tree segmentation produced by the LCTA method was performed in 3D Slicer (http://www.slicer.org) through the Lung CT Analyzer project (https://github.com/rbumm/SlicerLungCTAnalyzer/).DAS:No datasets were generated or analysed during the current study.en_US
dc.identifier.doi10.1186/s40537-024-00974-x
dc.identifier.issn2196-1115
dc.identifier.issue1en_US
dc.identifier.pmid39109339en_US
dc.identifier.scopus2-s2.0-85200482806en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s40537-024-00974-x
dc.identifier.urihttps://hdl.handle.net/11454/105407
dc.identifier.volume11en_US
dc.identifier.wosWOS:001283227000002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofJournal of Big Dataen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240831_Uen_US
dc.titleInterpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performanceen_US
dc.typeArticleen_US

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