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2.
Figure 2

Figure 2. Bland-Altman plot.. From: Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation.

Each point corresponds to an hippocampal segmentation. The difference between automatic and manual estimates is plotted against their average. The solid horizontal line corresponds to the average difference, and the dashed lines are plotted at average +/−1.96 standard deviations of the difference.

Albert K. Hoang Duc, et al. PLoS One. 2013;8(8):e70059.
3.
Figure 3

Figure 3. Hippocampal segmentation: automated (blue) vs manual (red).. From: Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation.

Overlapping area in purple. Row: (i) High case (Dice = 0.9398), (ii) Typical case (Dice = 0.9073), (iii) Low case (Dice = 0.8614). Column: (a) Coronal view, (b) Sagittal view, (c) Axial view.

Albert K. Hoang Duc, et al. PLoS One. 2013;8(8):e70059.
4.
Figure 1

Figure 1. Mean Dice’s similarity index computed for , , .. From: Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation.

Locally Linear Embedding is in blue, Isomap is in red and Laplacian Eigenmaps is in black. Solid lines represent the mean Dice’s similarity index, doted lines represents the standard deviation. Mean Dice’s similarity index against: () the number of atlases fused in STAPLE ( and fixed to best parameters), () the neighbourhood size in computing the manifold ( and fixed to best parameters), and () the manifold dimension ( and fixed to best parameters).

Albert K. Hoang Duc, et al. PLoS One. 2013;8(8):e70059.

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