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In Vitro Assessment of a 3D Segmentation Algorithm Based on the Belief Functions Theory in Calculating Renal Volumes by MRI

Pierre-Hugues Vivier1,2, Michael Dolores2, Isabelle Gardin2, Peng Zhang2, Caroline Petitjean2 and Jean-Nicolas Dacher1,2

1 Department of Radiology, University Hospital of Rouen, Irue de Germont, Rouen Cedex F-76031, France.
2 LITIS Laboratory, School of Medicine and Pharmacy, University of Rouen, Rouen, France.


Figure 1
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  		Fig. 1 —Schematic shows processing of stack of axial images.

 

Figure 2
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  		Fig. 2 —Flowchart of study shows that for each orientation (i.e., axial and coronal) and for each slice thickness, process was performed twice, with and without manual modifications. SDD = SD of the difference.

 

Figure 3
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  		Fig. 3 —Example of Bland-Altman graph used for accuracy assessment of MR segmentation method drawn from following experiment: 1-T unit, axial acquisition, 4-mm adjacent slices, observer 1's first measurements without manual modifications. Mean = 0.1 mL; SD of difference (SDD) = 1.07 mL; and 95% limits of agreement = 4 x 1.07 = 4.3 mL, corresponding to –2.1 and 2.2 mL.

 

Figure 4
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  		Fig. 4 —Accuracy of MR segmentation method. Plot shows maximal limits of agreement of MR-calculated renal volumes versus slice thickness. Data from axial acquisitions are shown in black, and data from coronal acquisitions are shown in gray. {diamondsuit} = no manual modification, – = manual modification allowed, 8* = 4-mm overlapped slices, {blacksquare} = mean MR measurement error.

 

Figure 5
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  		Fig. 5 —Intraobserver variability of MR segmentation method. Plot shows maximal limits of agreement of variability versus slice thickness. Data from axial acquisitions are shown in black, and data from coronal acquisitions are shown in gray. {diamondsuit} = no manual modification, – = manual modification allowed, 8* = 4-mm overlapped slices, {blacksquare} = mean intraobserver variability.

 

Figure 6
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  		Fig. 6 —Interobserver variability of MR segmentation method. Plot shows maximal limits of agreement of variability versus slice thickness. Data from axial acquisitions are shown in black, and data from coronal acquisitions are shown in gray. {diamondsuit} = no manual modification, – = manual modification allowed, 8* = 4-mm overlapped slices, {blacksquare} = mean interobserver variability.

 

Figure 7
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  		Fig. 7 —Mean number of modified images per stack. Black = axial slices, gray = coronal slices, 8* = 4-mm overlapped slices.

 

Figure 8
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  		Fig. 8A —Segmentation of intrasinus fat on coronal slice in pig kidney. Image shows appropriate semiautomatic segmentation of remaining intrasinus fat.

 

Figure 9
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  		Fig. 8B —Segmentation of intrasinus fat on coronal slice in pig kidney. Image shows manual modification applied to match fluid displacement measurement.

 

Figure 10
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  		Fig. 9 —Semiautomatic segmentation of right kidney in 6-month-old boy with ureterohydronephrosis. Upper row shows adjacent enhanced T1-weighted images on excretory phase. Lower row shows segmented renal parenchyma and excluded urinary tract.

 

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