Automatic segmentation of brain structures in longitudinal MR images of growing children
Aznar, Marianne Camille Bryce-Atkinson, Abigail Whitfield, Gillian A Vasquez Osorio, Eliana
Aznar, Marianne Camille
Bryce-Atkinson, Abigail
Whitfield, Gillian A
Vasquez Osorio, Eliana
Citations
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Abstract
Purpose or Objective
Children treated for brain tumours may suffer long-term cognitive damage and other sequelae after treatment.
Neuroscientists have developed open-access sophisticated software packages, such as FreeSurfer, for automatic
segmentation of brain sub-regions structures on MR images. Those tools are potentially of great interest for large
multicentric studies to retrospectively estimate the dose received by paediatric patients with brain tumours and evaluate
structure growth or atrophy. Here, we evaluate 1) the agreement between FreeSurfer structures and radiotherapy-specific
segmentation atlases, and 2) the sensitivity of FreeSurfer to capture volume changes due to aging.
Materials and Methods
20 healthy children, each imaged at approximately 5, 7 and 9 years, were selected from OpenNeuro data. All 60 T1-
weighted MR images (non-contrast, 1mm slice) were corrected for image inhomogeneity before automatic segmentation of
47 structures using FreeSurfer v7.1.1. The list of FreeSurfer structures was compared to the European Particle Therapy
Network atlas (EPTN, Eekers 2021) for concordance in definition. Segmentation quality was visually assessed by a single
observer for each image. Volumes of all substructures were calculated for each time point.
Results
15/47 FreeSurfer structures could be matched with the EPTN atlas: brainstem (divided into pons, midbrain, medulla
oblongata), optic chiasm, cerebellum, corpus callosum, ventricles (enabling the definition of the periventricular space),
and left/right hippocampi, caudate nuclei, thalami, and amygdalas. Segmentation quality was judged satisfactory for
retrospective dose estimation. Small volume changes were captured for the brainstem, bilateral hippocampi, bilateral thalami and right amygdala, where volume increased between ages 5 and 9 years by 9%, 5%, 4% and 6% respectively (average
between left and right for bilateral structures). The bilateral accumbens and caudate volumes appeared to decrease with
age, but the association was not significant, suggesting these structure volumes are relatively stable between these ages.
Whole brain volume was 10% larger in males vs females, leading to significantly smaller structures in females (p<0.05), in
the brainstem (6%), left accumbens (11%) as well as bilateral amygdalas (average 12%), hippocampi (average 9%), putamens
(average 7%) and thalami (average 8%) Conclusion
The quality of FreeSurfer segmentations was promising, and there was a modest overlap with EPTN atlas structures. The
comprehensive list of sub-regions (including e.g. grey and white matter segmentations) available in FreeSurfer could be of
interest for post-radiation outcome studies. The performance of FreeSurfer in the presence of tissue deformations (e.g.
tumour, surgery, treatment-related effects) is being investigated. In the future, we expect to use tools like FreeSurfer to
extract dose to substructures and sequential volume changes including brain atrophy post radiotherapy in large cohorts of
patients.
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Aznar M, Atkinson AB, Whitfield G, van Herk M, Osorio EV. Automatic segmentation of brain structures in longitudinal MR images of growing children. Radiotherapy and Oncology. 2022 May;170:S774-S5. PubMed PMID: WOS:000806764200408.