Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

David S. Paik; Christopher F. Beaulieu; Geoffrey D. Rubin; Burak Acar; R. Brooke Jeffrey; Judy Yee; Joyoni Dey; Sandy Napel

doi:10.1109/TMI.2004.826362

Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

David S. Paik, Christopher F. Beaulieu, Geoffrey D. Rubin, Burak Acar, R. Brooke Jeffrey, Judy Yee, Joyoni Dey, Sandy Napel

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

Abstract

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

Original language	English (US)
Pages (from-to)	661-675
Number of pages	15
Journal	IEEE Transactions on Medical Imaging
Volume	23
Issue number	6
DOIs	https://doi.org/10.1109/TMI.2004.826362
State	Published - Jun 2004
Externally published	Yes

Keywords

Colonic polyp
Computed tomography colonography (CTC)
Computer-aided detection (CAD)
Cross-validation
Lung nodule
Statistical shape model

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TMI.2004.826362

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title = "Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT",

abstract = "We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.",

keywords = "Colonic polyp, Computed tomography colonography (CTC), Computer-aided detection (CAD), Cross-validation, Lung nodule, Statistical shape model",

author = "Paik, {David S.} and Beaulieu, {Christopher F.} and Rubin, {Geoffrey D.} and Burak Acar and Jeffrey, {R. Brooke} and Judy Yee and Joyoni Dey and Sandy Napel",

note = "Funding Information: Manuscript received September 22, 2003; revised February 5, 2004. This work was supported in part by the National Institutes of Health (NIH) under Grant R01-CA72023 and Grant P41-RR09784. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was G. Wang. Asterisk indicates corresponding author. *D. S. Paik is with the Department of Radiology, Stanford University, Stanford, CA, USA 94305-5450 USA (e-mail: paik@smi.stanford.edu).",

year = "2004",

month = jun,

doi = "10.1109/TMI.2004.826362",

language = "English (US)",

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pages = "661--675",

journal = "IEEE Transactions on Medical Imaging",

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AU - Paik, David S.

AU - Beaulieu, Christopher F.

AU - Rubin, Geoffrey D.

AU - Acar, Burak

AU - Jeffrey, R. Brooke

AU - Yee, Judy

AU - Dey, Joyoni

AU - Napel, Sandy

N1 - Funding Information: Manuscript received September 22, 2003; revised February 5, 2004. This work was supported in part by the National Institutes of Health (NIH) under Grant R01-CA72023 and Grant P41-RR09784. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was G. Wang. Asterisk indicates corresponding author. *D. S. Paik is with the Department of Radiology, Stanford University, Stanford, CA, USA 94305-5450 USA (e-mail: paik@smi.stanford.edu).

PY - 2004/6

Y1 - 2004/6

N2 - We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

AB - We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

KW - Colonic polyp

KW - Computed tomography colonography (CTC)

KW - Computer-aided detection (CAD)

KW - Cross-validation

KW - Lung nodule

KW - Statistical shape model

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Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

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Keywords

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