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

  • Authors:

  • Yuhua Gu;Virendra Kumar;Lawrence O. Hall;Dmitry B. Goldgof;Ching-Yen Li;René Korn;Claus Bendtsen;Emmanuel Rios Velazquez;Andre Dekker;Hugo Aerts;Philippe Lambin;Xiuli Li;Jie Tian;Robert A. Gatenby;Robert J. Gillies

  • Affiliations:

  • Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA;Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA;Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA;Definiens AG, Trappentreustraíe 1, 80339 München, Germany;Discovery Sciences, AstraZeneca, 50S27 Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK;Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, The Netherlands;Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, The Netherlands;Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, The Netherlands;Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, The Netherlands;Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

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.