Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors

  • Authors:

  • Qi Song;Mingqing Chen;Junjie Bai;Milan Sonka;Xiaodong Wu

  • Affiliations:

  • Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA and Department of Radiation Oncology and Department of Ophthalmology & Visual Sciences;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA and Department of Radiation Oncology

  • Venue:
  • IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
  • Year:
  • 2011

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Abstract

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p