Automatic brain structure segmentation in children with brain tumours
Bryce-Atkinson, Abigail Wilson, L. J. Vasquez Osorio, Eliana Green, Andrew Whitfield, Gillian A McCabe, Martin G Merchant, T. E. Faught, A. M. Aznar, Marianne Camille
Bryce-Atkinson, Abigail
Wilson, L. J.
Vasquez Osorio, Eliana
Green, Andrew
Whitfield, Gillian A
McCabe, Martin G
Merchant, T. E.
Faught, A. M.
Aznar, Marianne Camille
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Abstract
Purpose or Objective
Auto-segmentation tools have been widely implemented in neuroimaging research, enabling extensive brain segmentations
to be obtained with little to no manual interaction. Applying these tools in paediatric radiotherapy research could enable
analyses that include a wider range of structures than are routinely delineated, be of benefit for standardising contours in
multi-centre studies and allow extensive dose-effect studies. These tools are developed in adults, so their applicability in
children with cancer is unclear due to age-related differences and the presence of the tumour and other pathology. This
study compares contours from three auto-segmentation tools in healthy children and in children with brain tumours.
Materials and Methods
We examined T1-weighted MRIs from 40 healthy children (age 5.0-16.4 years, median 9.3 years) and 40 children/young
adults with brain tumours (including medulloblastoma, low-grade glioma and astrocytoma; age 1.8-25.2 years, median 8.9
years). Segmentations of 15 subcortical structures (accumbens, amygdala, caudate, hippocampus, pallidum, putamen and
thalamus bilaterally, and brainstem) were generated by 3 open-source packages: FreeSurfer v7.2.0, the FMRIB Software
Library v6.0.5 FIRST tool (FSL), and the Computational Anatomy Toolbox v12.8 (CAT). Failed segmentations are reported
but excluded from further analyses. We assessed consistency between each package via comparison of each structure’s
centre-of-mass (CoM), Dice similarity coefficient (DSC), 95% Hausdorff distance and average contour distance. We
performed ANOVA to evaluate differences between each pairwise software comparison for each similarity metric, and t tests to compare differences between healthy children and children with brain tumours.
Results
Visual contour quality was acceptable (Figure 1). Segmentation failed in 11 cases (9 FSL, 1 FreeSurfer, 1 FreeSurfer/FSL),
predominantly due to atypical anatomy e.g. enlarged ventricles, or poor scan quality. CoM discrepancies and DSC scores
revealed significant differences (p <0.05) between FSL contours and both CAT and FreeSurfer, but not between CAT and
FreeSurfer. FSL contours were significantly different from FreeSurfer in average distance analyses and from CAT in
Hausdorff distance analyses. We found lower DSC scores, larger CoM and contour distances, and larger standard deviations
within each metric for every structure in children with brain tumours compared to healthy children. The difference was
significant in analysis considering all structures Conclusion
The greater magnitude and variation in similarity metrics in children with brain tumours suggests auto-segmentation tools
perform worse than in healthy children. Contour differences remained within 4mm in children with brain tumours.
FreeSurfer and CAT were the most consistent and showed the fewest failures, and therefore show promise for use in
paediatric radiotherapy research. Further work validating against clinical contours is needed.
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Bryce-Atkinson A, Wilson LJ, Osorio EV, Green A, Whitfield G, McCabe MG, et al. Automatic brain structure segmentation in children with brain tumours. Radiotherapy and Oncology. 2022 May;170:S1417-S8. PubMed PMID: WOS:000806779900446.