Journal article
A comparison of cranial cavity extraction tools for non-contrast enhanced CT scans in acute stroke patients
- Abstract:
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Cranial cavity extraction is often the first step in quantitative neuroimaging analyses. However, few automated, validated extraction tools have been developed for non-contrast enhanced CT scans (NECT). The purpose of this study was to compare and contrast freely available tools in an unseen dataset of real-world clinical NECT head scans in order to assess the performance and generalisability of these tools. This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (n = 20), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the quality of the results in a larger dataset (n = 428). Three tools were found; one of these had unreliable performance so subsequent evaluation was discontinued. The remaining tools included one that was adapted from the FMRIB software library (fBET) and a convolutional neural network- based tool (rBET). Quantitative comparison showed comparable accuracy, precision, recall and DSC values (fBET: 0.984 ± 0.002; rBET: 0.984 ± 0.003; p = 0.99) between the tools; however, intracranial volume was overestimated. Visual comparisons identified characteristic regional differences in the resulting cranial cavity segmentations. Overall fBET had highest visual quality ratings and was preferred by the readers in the majority of subject results (84%). However, both tools produced high quality extractions of the intracranial space and our findings should improve confidence in these automated CT tools. Pre- and post-processing techniques may further improve these results.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 3.1MB, Terms of use)
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- Publisher copy:
- 10.1007/s12021-021-09534-7
Authors
- Funder identifier:
- https://ror.org/0187kwz08
- Grant:
- SAPGF18/100031
- Publisher:
- Springer
- Journal:
- Neuroinformatics More from this journal
- Volume:
- 20
- Issue:
- 3
- Pages:
- 587-598
- Publication date:
- 2021-09-06
- Acceptance date:
- 2021-06-14
- DOI:
- EISSN:
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1559-0089
- ISSN:
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1539-2791
- Pmid:
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34490589
- Language:
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English
- Keywords:
- Pubs id:
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1196469
- Local pid:
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pubs:1196469
- Deposit date:
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2025-04-23
- ARK identifier:
Terms of use
- Copyright holder:
- Vass et al.
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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