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Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa)
a)Presented in part at the 2008 MICCAI Workshop: Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, New York.
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10.1118/1.3596529
/content/aapm/journal/medphys/38/7/10.1118/1.3596529
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/7/10.1118/1.3596529

Figures

Image of FIG. 1.
FIG. 1.

Interpolation implemented in our CAD system.

Image of FIG. 2.
FIG. 2.

Sphere simulation. (a) A sphere with a radius of r = 0.5, which is comparable to a 6 mm polyp. Curvatures of all surface points are examined. (b) A pair of spheres with a radius of 0.5. Curvatures of the surface points close to each other (highlighted regions) are examined.

Image of FIG. 3.
FIG. 3.

Comparison of mean curvatures (a) and Gaussian curvature (b) in xy-plane as a function of . Kernel method without interpolation causes a large error in mean curvature [spike in (a)] and Gaussian curvature [spike in (b)] at the location closest to the additional sphere. Curvature estimation with linear interpolation by a factor of 2 is more accurate.

Image of FIG. 4.
FIG. 4.

(a) FROC curves for 6–9 mm polyps on test data which show no interpolation and three interpolations. The curves show the benefits of the interpolation for small polyps. (b) jafroc FOM for different modalities. (modalities 1–4 represent CAD without interpolation, CAD with linear, quadratic B-spline and cubic B-spline interpolation, respectively.) Error bars present 95% confidence intervals.

Image of FIG. 5.
FIG. 5.

Polyps missed without interpolation but found with cubic B-spline interpolation at 10 FP per patient: (a) an 8 mm ademoma in sigmoid colon and (b) a 6 mm adenoma in sigmoid colon .

Image of FIG. 6.
FIG. 6.

FROC curves for 10 mm or larger polyps which show CAD performance with and without interpolation on test data. Three different interpolation methods are shown (linear, quadratic B-spline, cubic B-spline). Unlike the situation for detecting the small polyps, the curves show that interpolation does not significantly affect the performance of CAD for detecting large polyps.

Image of FIG. 7.
FIG. 7.

Clinical data example with a polyp present (a). Curvature classification without (b) and with cubic B-spline interpolation (c). Potential lesions are represented by bright regions. A normal colonic fold that abuts the polyp is indicated with an arrow in (a).

Tables

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TABLE I.

Curvature classification based on principal curvature values .

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TABLE II.

Mean curvature error , bias , and deviation and Gaussian curvature error , bias , and deviation on the sphere surface, with and without interpolations. Subscript 2 and 4 corresponds to interpolation by a factor of 2 or 4, respectively.

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TABLE III.

Mean curvature error , bias , and deviation and Gaussian curvature error , bias , and deviation on double spheres measured on the highlighted regions in Fig. 2(b), with and without interpolation.

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TABLE IV.

Comparing sensitivities at 10 FP per patient for CAD without and with interpolations for 6–9 mm polyps on test data.

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TABLE V.

Intermodality differences and 95% confidence intervals of jafroc FOM. If the 95% CI does not include 0, then the corresponding modality pairs are significantly different.

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/content/aapm/journal/medphys/38/7/10.1118/1.3596529
2011-06-30
2014-04-23
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa)
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/7/10.1118/1.3596529
10.1118/1.3596529
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