Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model

Figures & data

Figure 1. Segmentation of the SCAT layer from MRI scan: (from left to right) exterior geometry initialisation, segmented exterior geometry, segmented interior geometry, orthogonal slices of the segmented geometries.

Figure 2. The axial slices of the segmented geometries of the subcutaneous fat layer from MRI data-set of BMI 23 using the proposed model.

Figure 3. Segmentation of the subcutaneous fat layer from MRI scans (from left to right, increasing BMIs 19, 22, 23, 25, 35): the top row depicting coronal slices, and the bottom row depicting the manually labelled slices (yellow) and the corresponding axial slices segmented (orange) using the proposed model.

Table 1. Comparison of the segmentation of SCAT of the different MRI data-sets using the proposed model to manual segmentation, depicting the Dice similarity coefficient (DSC), sensitivity, specificity and overall accuracy of the segmented geometries (%).

	BMI	19	22	23	25	35
External geometry	DSC	96.6	95.5	97.5	95.6	92.1
	Sensitivity	94.9	94.6	96.9	94.8	91.8
	Specificity	99.8	99.4	99.7	99.4	98.0
	Overall	99.2	98.7	99.2	98.8	96.7
Internal geometry	DSC	94.5	93.3	95.6	92.5	88.6
	Sensitivity	95.4	95.8	95.5	91.3	88.7
	Specificity	99.3	98.7	99.4	99.2	97.8
	Overall	98.9	98.3	99.0	98.3	96.3

Figure 4. Subcutaneous fat thickness (mm) computed on the vertices of the exterior geometries: (from left to right) MRI data-sets of BMIs 19, 22, 23, 25 and 35.