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Research ArticleBrain

Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods

S. Li, F. Shi, F. Pu, X. Li, T. Jiang, S. Xie and Y. Wang
American Journal of Neuroradiology August 2007, 28 (7) 1339-1345; DOI: https://doi.org/10.3174/ajnr.A0620
S. Li
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F. Shi
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F. Pu
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X. Li
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T. Jiang
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S. Xie
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Y. Wang
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    Fig 1.

    The steps of parameterization and correspondence of hippocampus.

    A, Hippocampus shape characters; H and T represent the head and tail landmarks, respectively; the cyan shows isolatitude circles; the pink shows the extracted dateline.

    B, The parameterized mesh; the blue-red hue scale indicates the changes from the head to the tail of hippocampus.

    C, The alignment in terms of the isolatitude circle areas along latitude direction. The 2 curves on the left show the area distribution of isolatitude circles from head to tail; the red is the template and the green is 1 subject before transformation. The results after transformation are shown at the right.

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    Fig 2.

    The flow chart of LOOCV experiment; the numbers inside of the parentheses are the sample indexes.

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    Fig 3.

    LOOCV accuracy when selecting different repeatability of features to construct the classifier. The horizontal axis represents different repeatability of features, and the vertical axis represents the corresponding LOOCV accuracy. The different colors, respectively, represent the left and right hippocampi and different patch sizes. L represents the left hemisphere, and R represents the right hemisphere. The numbers behind L or R represent the patch sizes: 800 represents 20 × 40; 1250 represents 25 × 50; and 5000 represents 50 × 100.

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    Fig 4.

    Visualization of selected surface features by LOOCV experiment when the patch size is 50 × 100 in patients with AD compared with healthy control subjects. The first row shows the left and right hippocampi from the top view, whereas the second row shows them from the bottom view. Boundaries between the 3 zones of the hippocampal surface (ie, LZ, SZ, and IMZ) are drawn in black. LZ represents the lateral zone of the hippocampal surface, and approximates the CA1 subfield. SZ represents the superior zone approximating the combined CA2, CA3, and CA4 subfields and the gyrus dentatus, and IMZ represents the inferior-medial zone approximating the subiculum. All 3 zones are labeled. The white subregions represent no differences in the hippocampal surfaces between patients with AD and healthy control subjects. The significance of features is measured by the repeatability in the subsets. The more significant features are shaded on the purple-red hue scale shown in the third row. The purple represents lower significance, and the red represents higher significance.

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    Table 1:

    Demographics and clinical findings

    VariableAD (n = 19)Controls (n = 20)P
    Sex, F/M9/1010/10>.95*
    Age, mean ± SD72.6 ± 6.970.7 ± 6.4.38†
    Education, mean ± SD11.3 ± 4.010.9 ± 4.5.76†
    MMSE score, mean ± SD18.9 ± 3.929.5 ± 0.9<.0001†
    • Note:—MMSE indicates Mini-Mental State Examination; AD, Alzheimer disease; F, female; M, male.

    • * The P value was obtained by Pearson χ2 2-tailed test, with continuity correction for n < 5.

    • † The P value was obtained by a 2-sample 2-tailed t test.

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    Table 2:

    Cross-validation accuracies in the experiment

    Patch SizeSideLeave-1-Out Cross-Validation Accuracy, %*3-Fold Cross-Validation Accuracy*
    50 × 100Right84.683.4% (81.8%–84.9%)†
    Left94.984.5% (81.2%–87.9%)†
    25 × 50Right84.681.6% (79.8%–83.4%)†
    Left94.989.6% (87.1%–92.1%)†
    20 × 40Right84.682.6% (80.7%–84.6%)†
    Left94.989.3% (86.8%–91.9%)†
    • * Note:—All of the features appearing in subsets are selected to construct the classifier.

    • † The 95% confidence intervals of classification accuracy by 100 times cross-validation experiments.

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American Journal of Neuroradiology: 28 (7)
American Journal of Neuroradiology
Vol. 28, Issue 7
August 2007
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S. Li, F. Shi, F. Pu, X. Li, T. Jiang, S. Xie, Y. Wang
Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods
American Journal of Neuroradiology Aug 2007, 28 (7) 1339-1345; DOI: 10.3174/ajnr.A0620

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Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods
S. Li, F. Shi, F. Pu, X. Li, T. Jiang, S. Xie, Y. Wang
American Journal of Neuroradiology Aug 2007, 28 (7) 1339-1345; DOI: 10.3174/ajnr.A0620
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