Improved Curvature Estimation for Computer-aided Detection of Colonic Polyps in CT Colonography

doi:10.1016/j.acra.2011.03.012

Academic Radiology

Volume 18, Issue 8, August 2011, Pages 1024-1034

https://doi.org/10.1016/j.acra.2011.03.012 Get rights and content

Rationale and Objectives

Current schemes for computer-aided detection (CAD) of colon polyps usually use kernel methods to perform curvature-based shape analysis. However, kernel methods may deliver spurious curvature estimations if the kernel contains two surfaces, because of the vanished gradient magnitudes. The aim of this study was to use the Knutsson mapping method to deal with the difficulty of providing better curvature estimations and to assess the impact of improved curvature estimation on the performance of CAD schemes.

Materials and Methods

The new method was compared to two widely used kernel methods in terms of the performance of two stages of CAD: initial detection and true-positive and false-positive classification. The evaluation was conducted on a database of 130 computed tomographic scans from 67 patients. In these patient scans, there were 104 clinically significant polyps and masses >5 mm.

Results

In the initial detection stage, the detection sensitivity of the three methods was comparable. In the classification stage, at a 90% sensitivity level on the basis of the input of this step, the new technique yielded 3.15 false-positive results per scan, demonstrating reductions in false-positive findings of 30.2% (P < .01) and 27.9% (P < .01) compared to the two kernel methods.

Conclusions

The new method can benefit CAD schemes with reduced false-positive rates, without sacrificing detection sensitivity.

Section snippets

Methods

In this section, we outline three curvature estimation techniques: Knutsson mapping and two widely used kernel methods. The details of each technique can be found in the related references. The focus of this section is on the immunity property of Knutsson mapping to the discontinuity problem for CAD of colon polyps.

Phantom Study

By inspecting the plots in Figure 3, the error of a small object (theoretically with a larger curvature) is higher than that of a large object, which agrees with the results of Rieger et al (23). For objects with different sizes, the optimal parameters of σ_T and σ_k may vary. The optimal values are about σ_T = 4 and σ_k = 1.

So far, for the three curvature estimation methods, we have obtained their optimal parameters, which are (0.7, 0.1) for (α₁, α₂), (1, 2) for (σ₁, σ₂), and (1, 4, 1) for (σ_g, σ_T

Phantom Study

For two traditional kernel methods, KM₁ and KM₂, the optimal parameters have been extensively explored in previous works. In this study, we focused on investigating the optimal configuration for a new method, KM_M. Bearing in mind that the purpose of the investigation was for CAD in CTC, we constructed phantom images with similar image contrast and noise level to those of clinical patient images. The phantom sizes are specified analogously to the sizes of typical polyps in the CTC database (

Conclusion

With the two widely used kernel methods, KM₁ and KM₂, spurious calculations in curvature estimation were frequently observed (22) because of the gradient discontinuity problem, indicating false high curvature. In this study, we applied a new method, KM_M, to improve the curvature computation and investigated the potential benefits of KM_M for CAD of colon polyps on CTC.

From the results of the phantom study, the new method with optimized parameters greatly improved the curvature estimation for the

Acknowledgment

We would like to acknowledge the use of the Viatronix V3D-Colon Module (Viatronix, Inc, Stony Brook, NY).

References (31)

R. Summers et al.
Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population
Gastroenterology
(2005)
J.P. Thirion et al.
Computing the differential characteristics of isointensity surfaces
Comput Vis Image Understand
(1995)
S. Campbell et al.
Analysis of kernel method for surface curvature estimation
Int Congr Ser
(2004)
P. Sundaram et al.
Colon polyp detection using smoothed shape operator: preliminary results
Med Image Anal
(2008)
Cancer facts & figures 2008
(2008)
D. Eddy
Screening for colorectal cancer
Ann Intern Med
(1990)
T. Gluecker et al.
Colorectal cancer screening with CT colonography, colonoscopy, and double-contrast barium enema examination: prospective assessment of patient perceptions and preferences
Radiology
(2003)
B. Levi et al.
Emerging technologies in screening for colorectal cancer: CTC, immunochemical fecal occult blood tests, stool screening using molecular markers
CA Cancer J Clin
(2002)
P. Pickhardt et al.
Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults
N Engl J Med
(2003)
G. Kiss et al.
Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods
Eur Radiol
(2002)

R. Summers et al.

Automated polyp detector for CT colonography: feasibility study

Radiology

(2000)

S. Taylor et al.

Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography

AJR Am J Roentgenol

(2006)

D. Bielen et al.

Computer-aided detection for CT colonography: update 2007

Abdom Imaging

(2007)

Yoshida H, Nappi J. CAD in CT colonography: past, present, and future. Presented at: 11th International Conference of...

O. Monga et al.

From voxel to intrinsic surface features

Image Vis Comput

(1992)

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: This work was partially supported by grants CA082402 and CA120917 from the National Cancer Institute (Bethesda, MD).

: Dr Lu is supported by the National Nature Science Foundation of China under grant 60772020.

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Original investigationImproved Curvature Estimation for Computer-aided Detection of Colonic Polyps in CT Colonography

Rationale and Objectives

Materials and Methods

Results

Conclusions

Section snippets

Methods

Phantom Study

Phantom Study

Conclusion

Acknowledgment

Gastroenterology

Comput Vis Image Understand

Int Congr Ser

Med Image Anal

Cancer facts & figures 2008

Screening for colorectal cancer

Ann Intern Med

Colorectal cancer screening with CT colonography, colonoscopy, and double-contrast barium enema examination: prospective assessment of patient perceptions and preferences

Radiology

Emerging technologies in screening for colorectal cancer: CTC, immunochemical fecal occult blood tests, stool screening using molecular markers

CA Cancer J Clin

Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults

N Engl J Med

Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods

Eur Radiol

Automated polyp detector for CT colonography: feasibility study

Radiology

Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography

AJR Am J Roentgenol

Computer-aided detection for CT colonography: update 2007

Abdom Imaging

From voxel to intrinsic surface features

Image Vis Comput

Original investigation
Improved Curvature Estimation for Computer-aided Detection of Colonic Polyps in CT Colonography