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UBC Theses and Dissertations

Evaluation of automated hippocampal segmentation software in a clinically heterogeneous sample MacRae, Cassie Brieana


Accurate, efficient processing of magnetic resonance images (MRI) offers potential value for both basic and clinical neuroscience; however, the current gold standard method of doing so is manual segmentation, a labor-intensive process with inherent variability. Limitations associated with manual segmentation have motivated the development of automated software programs for structural MRI processing and volumetric analysis of subcortical brain structures. The three most commonly used platforms are FSL, FreeSurfer, and SPM. As each platform uses a unique mathematical algorithm for image analysis, it is necessary to determine which program offers optimal subcortical segmentation, particularly for high field-strength imaging. The current study compared automated volumetric analysis of the human hippocampus with manual segmentation values in a clinically complex population in order to determine which method provides the most robust results in the presence of motion artifact, a common problem in MR, and visible anatomic anomalies of the hippocampi. MRI data included in the current study was obtained from residents of Vancouver B.C’s Downtown Eastside (DTES) social housing projects, the majority of whom experience co-occurring addiction/polysubstance abuse and severe mental illness. A large number of these MRI scans are characterized by abnormal hippocampal morphology, perhaps resultant of neuropsychiatric illness, addiction, infection, or interactions of these factors. Intraclass correlations (ICC) were used to determine which set of automated hippocampal volumes best correlated with manual measures in motion artifact-free MRI data with and without bilateral abnormal hippocampal cavities. FreeSurfer volumes were most correlated with manual measurements regardless of whether MRI data included hippocampal cavities or not (ICC=0.88, no cavities; 0.48 with cavities), followed by FSL/FIRST (ICC=0.64, no cavities; ICC=0.43, with cavities), and SPM8.0/AAL (ICC=0.039, with cavities; ICC=0.035, no cavities). FSL/FIRST was the most sensitive to severe motion artifact (ICC=0.62), followed by FreeSurfer 5.1 (ICC=0.85), manual segmentation (ICC=0.96), and SPM8.0/AAL (ICC=0.99). ICCs for FreeSurfer 5.1 vs. FSL/FIRST were 0.87 (no motion artifact, no hippocampal cavities), 0.87 (no motion artifact, bilateral hippocampal spaces), and 0.64 (MRI scans with severe motion artifact). These data suggest automated segmentation methods are sensitive to MRI data with compromised image quality and deviations in normal hippocampal morphology.

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