A comparative study of segmentation techniques for the quantification of brain subcortical volume.

Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not b...

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Publicado en:Brain Imaging & Behavior Vol. 12; no. 6; pp. 1678 - 1696
Autores principales: Akudjedu, Theophilus N., Nabulsi, Leila, Makelyte, Migle, Scanlon, Cathy, Hehir, Sarah, Casey, Helen, Ambati, Srinath, Kenney, Joanne, O'Donoghue, Stefani, McDermott, Emma, Kilmartin, Liam, Dockery, Peter, McDonald, Colm, Hallahan, Brian, Cannon, Dara M.
Formato: research Journal Article
Publicado: Springer Nature Dec2018
Acceso en línea:Ver este registro en EBSCOhost
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          Akudjedu, Theophilus N.
          Nabulsi, Leila
          Makelyte, Migle
          Scanlon, Cathy
          Hehir, Sarah
          Casey, Helen
          Ambati, Srinath
          Kenney, Joanne
          O'Donoghue, Stefani
          McDermott, Emma
          Kilmartin, Liam
          Dockery, Peter
          McDonald, Colm
          Hallahan, Brian
          Cannon, Dara M.
        affil: Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, Psychiatry & Anatomy, School of Medicine,College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91TK33, Galway, Ireland
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          Brain
          Magnetic Resonance Imaging Methods
          Image Processing, Computer Assisted Methods
          Adult
          Brain Anatomy and Histology
          Middle Age
          Body Weights and Measures
          Mental Disorders
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          Female
          Male
          Human
          Brain Pathology
          Software
          Mental Disorders Pathology
          Information Science
          Adolescence
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Scales
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Adolescent: 13-18 years
          Female
          Male
      ab: Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not been systematically investigated in large samples. We compared the accuracy of fully automated [(i) model-based: FSL-FIRST; (ii) patch-based: volBrain], semi-automated (FreeSurfer) and stereological (Measure®) segmentation techniques with manual tracing (ITK-SNAP) for delineating volumes of the caudate (easy-to-segment) and the hippocampus (difficult-to-segment). High resolution 1.5 T T1-weighted MR images were obtained from 177 patients with major psychiatric disorders and 104 healthy participants. The relative consistency (partial correlation), absolute agreement (intraclass correlation coefficient, ICC) and potential technique bias (Bland-Altman plots) of each technique was compared with manual segmentation. Each technique yielded high correlations (0.77-0.87, p < 0.0001) and moderate ICC's (0.28-0.49) relative to manual segmentation for the caudate. For the hippocampus, stereology yielded good consistency (0.52-0.55, p < 0.0001) and ICC (0.47-0.49), whereas automated and semi-automated techniques yielded poor ICC (0.07-0.10) and moderate consistency (0.35-0.62, p < 0.0001). Bias was least using stereology for segmentation of the hippocampus and using FreeSurfer for segmentation of the caudate. In a typical neuropsychiatric MRI dataset, automated segmentation techniques provide good accuracy for an easy-to-segment structure such as the caudate, whereas for the hippocampus, a reasonable correlation with volume but poor absolute agreement was demonstrated. This indicates manual or stereological volume estimation should be considered for studies that require high levels of precision such as those with small sample size.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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