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Items: 4

1.
Figure 1

Figure 1. From: Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI.

Initialisation: a. extraction of the bounding boxes from the probabilistic atlases, b. extraction of the initial objects from each probabilistic atlas through conditional pruning.

Marie Chupin, et al. Hippocampus. ;19(6):579-587.
2.
Figure 2

Figure 2. From: Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI.

3D-renderings of manual, atlas-derived and automatic segmentations, overlap between segmentations (manual segmentations in shades of grey) and probabilistic atlases, for the best and worst results amongst 8 patients with AD.

Marie Chupin, et al. Hippocampus. ;19(6):579-587.
3.
Figure 3

Figure 3. From: Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI.

Experiment 1: Automatically computed (left+right) volumes for the CN (red + for correct segmentations, * for acceptable segmentations and - for the unsatisfactory segmentation) and AD (blue circles for correct segmentations, squares for acceptable segmentations and triangles for unsatisfactory segmentations) as a function of age.

Marie Chupin, et al. Hippocampus. ;19(6):579-587.
4.
Figure 4

Figure 4. From: Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI.

Experiment 3: Automatically computed volumes corresponding to the classification experiments: a: CN (red crosses) and AD, b. CN (red crosses) and MCI, c. CN at 18 months (red crosses) and MCI converting at 18 months, d. MCI not converting (red crosses) and MCI converting. {x, disc} for correct segmentations, {*, square} for acceptable segmentations and {−, triangle} for unsatisfactory segmentations

Marie Chupin, et al. Hippocampus. ;19(6):579-587.

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