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Automated CT-based segmentation and quantification of total intracranial volume

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images.

Materials and methods

Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph.

Results

We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson’s correlation coefficient was r = 0.94, tICVml[manual] = 1.05 × tICVml[automated] - 33.78 (R2 = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL.

Conclusions

tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI.

Key Points

• Automated estimation of tICV is in good agreement with manual tracing.

• Consistent tICV estimations from repeated measurements demonstrate the robustness of the algorithm.

• Automatically segmented volumes seem less variable than those from manual tracing.

• Unbiased and automated tlCV estimation is possible from CT.

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Abbreviations

SD:

Standard deviation

tICV:

Total intracranial volume

CT:

Computed tomography

MRI:

Magnetic resonance imaging

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Acknowledgments

The scientific guarantor of this publication is Dr. Eric Westman. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. This study has received funding from the Swedish Brain Power, the Strategic Research Programme in Neuroscience at Karolinska Institutet, the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Society of Medicine, Loo och Hans Ostermans stiftelse för medicinsk forskning, Stiftelsen för ålderssjukdomar vid Karolinska Institutet, Karolinska Institutets foskningsbidrag, Axel och Signe Lagermans donationsstiftelse. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. This data has not been published elsewhere, except in abstract form. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

Author information

Authors and Affiliations

  1. Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, Novum, Blickagången 6, 14157, Stockholm, Sweden

    Carlos Aguilar, Lars-Olof Wahlund & Eric Westman

  2. Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Karolinska Institute, Stockholm, Sweden

    Kaijsa Edholm, Lena Cavallin, Susanne Muller & Rimma Axelsson

  3. Department of Radiology, Karolinska University Hospital in Huddinge, Stockholm, Sweden

    Kaijsa Edholm, Lena Cavallin, Susanne Muller & Rimma Axelsson

  4. Institute of Psychiatry, King’s College London, London, UK

    Andrew Simmons

  5. NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London, UK

    Andrew Simmons

  6. Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden

    Ingmar Skoog

  7. Department of Surgical Sciences, Radiology, Akademiska Sjukhuset, Uppsala University, Uppsala, Sweden

    Elna-Marie Larsson

Authors
  1. Carlos Aguilar
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  2. Kaijsa Edholm
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  3. Andrew Simmons
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  4. Lena Cavallin
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  5. Susanne Muller
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  7. Elna-Marie Larsson
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  8. Rimma Axelsson
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  9. Lars-Olof Wahlund
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  10. Eric Westman
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Corresponding author

Correspondence to Carlos Aguilar.

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Aguilar, C., Edholm, K., Simmons, A. et al. Automated CT-based segmentation and quantification of total intracranial volume. Eur Radiol 25, 3151–3160 (2015). https://doi.org/10.1007/s00330-015-3747-7

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  • DOI: https://doi.org/10.1007/s00330-015-3747-7

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