Accuracy assessment of CT-based outer surface femur meshes

Figures & data

Table I.  CT surface mesh classes, using the self-developed Filter & Mesh procedure of Gelaude et al. [25]. (pps: points per spline; spAcc: spline accuracy value; IsDist: interslice distance. Each class consists of 15 entire femur meshes; notice that a smaller spline accuracy value (spAcc) designates a higher spline accuracy.) Note: from this table, “groups” of classes were defined to investigate the influence of one parameter setting when all others were kept unchanged, e.g., groups 215, 316 and 347, which check the influence of a varying IsDist, spAcc and pps, respectively.

	Segmentation parameter settings			Number of triangulation faces
Class ID	pps	spAcc	IsDist	Mean	Std	Max	Min
1	35	1.0	2	15532	482	16693	14873
2	35	1.0	1	30387	960	32653	29013
3	35	0.4	2	15532	481	16693	14873
4	50	0.4	2	22186	688	23848	21247
5	35	1.0	3	10605	283	11303	10253
6	35	1.6	2	15535	481	16693	14872
7	75	0.4	2	33282	1031	35773	31873

Table II.  CT surface mesh classes, using Mimics® (Version 10.0). (resolution = with regard to matrix reduction, how many voxels were geometrically merged and averaged by their intensity value; * 2 iterations, smoothing factor 0.3; ^** advance edge reduction algorithm, tolerance (pixel size)/8, edge angle 10, iteration 3. All classes use the contour interpolation method (2D partial volume compensation) and an interslice distance of 1 mm; each class consists of 15 entire femur meshes).

	Segmentation parameter settings				Number of triangulation faces
Class ID	XY resolution	Z resolution	Smoothing	Triangle reduction	Mean	Std	Max	Min
L	6	2	no	no	15658	2086	20884	12582
m	3	2	no	no	32726	5023	44468	26136
h	2	1	no	no	95497	15466	130116	75972
o	1	1	yes*	yes^**	66367	17156	100160	48038

Figure 1. Experimental set-up for optical surface scanning with the Metris LC50, mounted on a Coord3 MC16 coordinate measuring machine. The dry femur is mounted by inserting a rigid pin distally in the intramedullary canal, and is positioned obliquely with respect to the ground plane. Pre-drilling of the femur with a slightly smaller diameter than the pin diameter ensures rotational stability of the bone.

Figure 2. Comparison scheme. To assess the combined influence of soft tissue and boiling on the comparisons “A” between dry bone ground truths and bone meshes segmented from intact cadaver CT scans, additional dry bone CT scans are similarly processed (“B”) by one of the class L, m or h segmentation algorithms (Table II). Relative comparison of the 3D deviation statistics (mean X, standard deviation σ) for A and B then cancels out the contribution of the segmentation algorithm, and–if the same scanner and settings are used–of the CT machine as well.

Figure 3. Graphs showing 3D deviations of CT meshes for the entire femur. (pps = points per spline; spAcc = spline accuracy value; IsDist = interslice distance. Note that a smaller spline accuracy value (spAcc) designates a higher spline accuracy.) (See Tables I and II for class ID and Table III for statistics.) [Color version available online.]

Table III.  Three-dimensional deviations of cadaver CT femur meshes (in mm). (X: mean; σ: standard deviation; A: see Figure 2; class ID: see Tables I and II; n: number of measurements; a negative 3D deviation indicates that the CT mesh is inflated.)

	Entire femur
Class ID	XA	± σA	Max	Min	n
1	−0.71	±0.56	4.39	−5.20	1111498
2	−0.69	±0.54	4.11	−5.08	1111290
3	−0.70	±0.55	4.34	−5.17	1111092
4	−0.72	±0.53	4.24	−5.30	1110876
5	−0.71	±0.58	4.78	−5.57	1111226
6	−0.72	±0.60	4.47	−5.34	1112331
7	−0.73	±0.52	4.16	−5.14	1110579
L	−3.10	±0.79	2.63	−8.15	1113160
m	−1.66	±0.55	3.29	−6.06	1111341
h	−1.14	±0.50	2.96	−5.26	1111555
o	−0.62	±0.49	3.40	−4.79	1112266
	Proximal femur
Class ID	XA	± σA	Max	Min	n
1	−0.77	±0.53	2.47	−3.99	432517
2	−0.74	±0.51	2.42	−3.89	432429
3	−0.76	±0.52	2.49	−3.98	432376
4	−0.78	±0.50	2.47	−4.02	432213
5	−0.77	±0.54	2.45	−4.02	432523
6	−0.78	±0.56	2.46	−4.07	432854
7	−0.79	±0.49	2.46	−4.02	432157
L	−3.14	±0.77	0.78	−6.72	432791
m	−1.71	±0.52	1.56	−4.77	432226
h	−1.18	±0.49	2.13	−4.36	432276
o	−0.67	±0.45	2.11	−3.43	432383
	Diaphyseal femur
Class ID	XA	± σA	Max	Min	n
1	−0.70	±0.32	1.91	−2.71	617847
2	−0.69	±0.31	1.84	−2.46	617962
3	−0.69	±0.29	1.78	−2.62	617288
4	−0.71	±0.28	1.94	−2.63	617198
5	−0.71	±0.32	1.88	−2.72	617866
6	−0.72	±0.38	2.12	−2.85	618728
7	−0.72	±0.28	1.90	−2.63	617256
L	−3.27	±0.47	−0.29	−5.38	618213
m	−1.73	±0.33	0.84	−3.73	617848
h	−1.18	±0.31	0.95	−2.71	618167
o	−0.62	±0.35	1.67	−2.25	618852
	Distal femur
Class ID	XA	± σA	Max	Min	n
1	−0.62	±0.80	6.98	−7.20	439270
2	−0.61	±0.76	6.55	−7.06	439091
3	−0.62	±0.79	6.93	−7.16	439150
4	−0.64	±0.76	6.75	−7.51	438854
5	−0.63	±0.83	7.57	−7.52	439242
6	−0.64	±0.83	7.05	−7.28	439542
7	−0.65	±0.75	6.66	−7.19	438890
L	−2.93	±0.99	5.14	−9.99	440391
m	−1.54	±0.75	5.74	−7.86	439248
h	−1.04	±0.68	4.99	−7.12	439384
o	−0.55	±0.67	5.43	−6.67	439591

Table IV.  Three-dimensional deviations of dry bone CT meshes (in mm) using segmentation algorithm of class h (see Table II) (X: mean; σ: standard deviation; n: number of measurements; a negative 3D deviation indicates that the CT mesh is inflated. For “A” and “B”, see Figure 3).

	XB	± σB	Max	Min	n	Δ XAB
Entire femur	−0.65	±0.31	3.03	−3.84	1115113	+0.49
Proximal part	−0.65	±0.30	1.01	−2.17	433181	+0.53
Diaphyseal part	−0.68	±0.23	0.79	−2.08	619722	+0.50
Distal part	−0.62	±0.40	4.89	−4.84	440244	+0.42

Figure 4. Influence of soft tissue removal and boiling of the cadaver bones. (a) For class h (see Table II and the section headed Assessing influence of soft tissue presence and boiling), the dry bone CT scans (B) as well as the intact specimen CT scans (A) of all specimens are segmented and compared to the MetrisScan ground truths (entire femora). The mean 3D deviation X_B is attributed to the CT machine and segmentation algorithm; apart from the same CT machine and segmentation algorithm, the mean 3D deviation X_A is attributed to soft tissue presence (setting the bone Hounsfield window for cortical bone) and boiling of the cadaver bones. (b) Graphs of the 3D deviations for all intact specimen CT-based meshes, all classes (entire femora). A positive shift Δ X_AB should be applied to the graphs to counteract the combined effect of soft tissue presence and boiling. The dotted vertical line should serve as new zero line. [Color version available online.]

Table V.  Optimal choice of segmentation parameters for outer surface mesh generation for femoral CT scans with soft tissue, as in clinical practice. (See Tables I and II for class definitions; voxel in-slice dimension = 1 mm × 1 mm; a negative 3D deviation indicates that the CT mesh is inflated; Δ X_AB = compensation for combined effect of soft tissue presence during bone Hounsfield windowing and shrinkage of the ground truth femora by boiling [see Table IV].)

	Filter & Mesh base platform [25](mm)	Mimics® approach(mm)	Δ XAB (mm)
Entire femur mesh	class 3	class o
	−0.70 ± 0.55 (15532 ± 481 triangles)	−0.62 ± 0.49 (66367 ± 17156 triangles)	+0.49
Proximal part	class 2	class o
	−0.74 ± 0.51 (10770 ± 452 triangles)	−0.67 ± 0.45 (30816 ± 10957 triangles)	+0.53
Diaphyseal part	class 3	class o
	−0.69 ± 0.29 (10967 ± 356 triangles)	−0.62 ± 0.35 (21981 ± 3727 triangles)	+0.50
Distal part	class 2	class o
	−0.61 ± 0.76 (9750 ± 271 triangles)	−0.55 ± 0.67 (28083 ± 7912 triangles)	+0.42

Gelaude F, Vander Sloten J, Lauwers B. Semi-automated segmentation and visualisation of outer bone cortex from medical images. Comput Methods Biomech Biomed Engin 2006; 9(1)65–77

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