Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach

被引：112

作者：

Gu, Yuhua ^{[1
]}

Kumar, Virendra ^{[1
]}

Hall, Lawrence O. ^{[2
]}

Goldgof, Dmitry B. ^{[2
]}

Li, Ching-Yen ^{[2
]}

Korn, Rene ^{[3
]}

Bendtsen, Claus ^{[4
]}

Velazquez, Emmanuel Rios ^{[5
]}

Dekker, Andre ^{[5
]}

Aerts, Hugo ^{[5
]}

Lambin, Philippe ^{[5
]}

Li, Xiuli ^{[6
]}

Tian, Jie ^{[6
]}

Gatenby, Robert A. ^{[1
]}

Gillies, Robert J. ^{[1
]}

机构：

[1] H Lee Moffitt Canc Ctr & Res Inst, Dept Imaging, Tampa, FL 33612 USA

[2] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA

[3] Definiens AG, D-80339 Munich, Germany

[4] AstraZeneca, Discovery Sci, Macclesfield SK10 4TG, Cheshire, England

[5] Univ Hosp Maastricht, Dept Radiat Oncol, Maastricht, Netherlands

[6] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Med Image Proc Grp, Beijing 100190, Peoples R China

来源：

PATTERN RECOGNITION | 2013年 / 46卷 / 03期

基金：

美国国家卫生研究院;

关键词：

Image features; Delineation; Lung tumor; Lesion; CT; Region growing; Ensemble segmentation; ACTIVE CONTOUR; INTERACTIVE SEGMENTATION; VARIABILITY; CANCER;

D O I：

10.1016/j.patcog.2012.10.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A single click ensemble segmentation (SCES) approach based on an existing "Click & Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：692 / 702

页数：11

共 44 条

[1]

[Anonymous], 1999, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science

[2]

Athelogou Maria, 2007, P407, DOI 10.1007/978-3-540-71331-9_15

[3] X-Ray Computed Tomography: Semiautomated Volumetric Analysis of Late-Stage Lung Tumors as a Basis for Response Assessments [J].

Bendtsen, C. ;

Kietzmann, M. ;

Korn, R. ;

Mozley, P. D. ;

Schmidt, G. ;

Binnig, G. .

INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING, 2011, 2011

[4] Fast approximate energy minimization via graph cuts [J].

Boykov, Y ;

Veksler, O ;

Zabih, R .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2001, 23 (11) :1222-1239

[5] Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening [J].

Chen, C. ;

Li, H. ;

Zhou, X. ;

Wong, S. T. C. .

JOURNAL OF MICROSCOPY, 2008, 230 (02) :177-191

[6] A level set method based on the Bayesian risk for medical image segmentation [J].

Chen, Yao-Tien .

PATTERN RECOGNITION, 2010, 43 (11) :3699-3711

[7] An evaluation of the variability of tumor-shape definition derived by experienced observers from CT images of supraglottic carcinomas (ACRIN protocol 6658) [J].

Cooper, Jay S. ;

Mukherji, Suresh K. ;

Toledano, Alicia Y. ;

Beldon, Clifford ;

Schmalfuss, Ilona M. ;

Amdur, Robert ;

Sailer, Scott ;

Loevner, Laurie A. ;

Kousouboris, Phil ;

Ang, K. Kian ;

Cormack, Jean ;

Sicks, JoRean .

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2007, 67 (04) :972-975

[8] Segmentation of pulmonary nodules in thoracic CT scans: A region growing approach [J].

Dehmeshi, Jamshid ;

Amin, Hamdan ;

Valdivieso, Manlio ;

Ye, Xujiong .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2008, 27 (04) :467-480

[9]

Dijkers JJ, 2005, LECT NOTES COMPUT SC, V3749, P712

[10] Individual tooth segmentation from CT images using level set method with shape and intensity prior [J].

Gao, Hui ;

Chae, Oksam .

PATTERN RECOGNITION, 2010, 43 (07) :2406-2417

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