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Brain Structure and Function

, Volume 224, Issue 2, pp 795–810 | Cite as

Accuracy and bias of automatic hippocampal segmentation in children and adolescents

  • Annika Herten
  • Kerstin Konrad
  • Helga Krinzinger
  • Jochen Seitz
  • Georg G. von PolierEmail author
Original Article
  • 138 Downloads

Abstract

The hippocampus (Hc) is of great importance in various psychiatric diseases in adults, children and adolescents. Automated Hc segmentation has been widely used in adults, implying sufficient overlap with manual segmentation. However, estimation biases related to the Hc volume have been pointed out. This may particularly apply to children who show age-related Hc volume changes, thus, questioning the accuracy of automated Hc segmentation in this age group. The aim of this study was to compare manual segmentation with automated segmentation using the widely adopted FreeSurfer (FS) and MAGeT-Brain software. In 70 children and adolescents (5–16 years, mean age 10.6 years), T1-weighted images were acquired on one of two identical 3T scanners. Automated segmentation was performed using the FS subcortical segmentation, the FS hippocampal subfields segmentation and the MAGeT-Brain software. In comparison with manual segmentation, volume differences, Dice similarity coefficient (DSC), Bland–Altman plot, intraclass correlation coefficient (ICC) and left–right consistency of automated segmentation were calculated. The average percentage of volume differences (PVD) with manual segmentation was 56.8% for FS standard segmentation, 32.2% for FS subfield segmentation and − 15.6% for MAGeT-Brain. The FS Hc subfields segmentation (left/right DSC = 0.86/0.87) and MAGeT-Brain (both hemispheres DSC = 0.91) resulted in a higher volume overlap with manual segmentation compared with the FS subcortical segmentation (DSC = 0.79/0.78). In children aged 5–10.5 years, MAGeT-Brain yielded the highest overlap (DSC = 0.92/0.93). Contrary volume estimation biases were detected in FS and MAGeT-Brain: FS showed larger volume overestimation in smaller Hc volumes, while MAGeT-Brain showed more pronounced volume underestimation in larger Hc volumes. While automated Hc segmentation using FS hippocampal subfields or MAGeT-Brain resulted in adequate volume overlap with manual segmentation, estimation biases compromised the reliability of automated procedures in children and adolescents.

Keywords

Pediatric population FreeSurfer MAGeT-Brain Limbic system Morphometry Manual segmentation 

Notes

Funding

This work was supported by the Federal Ministry of Education and Research [grant number 01GJ0808]; the “Interdisciplinary center for clinical studies, IZKF RWTH Aachen University” [grant number N4-1]. Automatic segmentations were performed with computing resources granted by RWTH Aachen University under project rwth0151. The authors wish to thank all children and their parents for participating in this study.

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

429_2018_1802_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (DOCX 20 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department for NeurosurgeryUniversitätsklinikum EssenEssenGermany
  2. 2.Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity Clinic Rheinisch-Westfälische Technische Hochschule AachenAachenGermany
  3. 3.Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity Clinic Rheinisch-Westfälische Technische Hochschule AachenAachenGermany
  4. 4.JARA-Brain Institute (JBI-II) Molecular Neuroscience and Neuroimaging, Research Centre JülichJülichGermany

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