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Mansoor NM, Vanniyasingam T, Malone I, Hobbs NZ, Rees E, Durr A, Roos RAC, Landwehrmeyer B, Tabrizi SJ, Johnson EB, Scahill RI. Validating Automated Segmentation Tools in the Assessment of Caudate Atrophy in Huntington's Disease. Front Neurol 2021; 12:616272. [PMID: 33935934 PMCID: PMC8079754 DOI: 10.3389/fneur.2021.616272] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric measures the current gold standard is manual delineation, which is unfeasible for samples sizes required for large clinical trials. Methods: Using a cohort of early Huntington's disease (HD) patients (n = 46) and controls (n = 35), we compared the performance of four automated segmentation tools (FIRST, FreeSurfer, STEPS, MALP-EM) with manual delineation for generating cross-sectional caudate volume, a region known to be vulnerable in HD. We then examined the effect of each of these baseline regions on the ability to detect change over 15 months using the established longitudinal Caudate Boundary Shift Integral (cBSI) method, an automated longitudinal pipeline requiring a baseline caudate region as an input. Results: All tools, except Freesurfer, generated significantly smaller caudate volumes than the manually derived regions. Jaccard indices showed poorer levels of overlap between each automated segmentation and manual delineation in the HD patients compared with controls. Nevertheless, each method was able to demonstrate significant group differences in volume (p < 0.001). STEPS performed best qualitatively as well as quantitively in the baseline analysis. Caudate atrophy measures generated by the cBSI using automated baseline regions were largely consistent with those derived from a manually segmented baseline, with STEPS providing the most robust cBSI values across both control and HD groups. Conclusions: Atrophy measures from the cBSI were relatively robust to differences in baseline segmentation technique, suggesting that fully automated pipelines could be used to generate outcome measures for clinical trials.
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Affiliation(s)
- Nina M Mansoor
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Tishok Vanniyasingam
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ian Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Nicola Z Hobbs
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Elin Rees
- IXICO plc, Griffin Court, Long Lane, London, United Kingdom
| | - Alexandra Durr
- Sorbonne Université, Institut du Cerveau/Paris Brain Institute AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France
| | - Raymund A C Roos
- Department of Neurology, Leiden University Medical Centre, Leiden, Netherlands
| | | | - Sarah J Tabrizi
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Eileanoir B Johnson
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Rachael I Scahill
- Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
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