Automatic segmentation of the diaphragm in non-contrast CT images

  • Authors:

  • Raja Yalamanchili;Deepak Chittajallu;Paul Balanca;Balaji Tamarappoo;Daniel Berman;Damini Dey;Ioannis Kakadiaris

  • Affiliations:

  • Computational Biomedicine Lab., Dept. of Computer Science, Univ. of Houston, Houston, TX;Computational Biomedicine Lab., Dept. of Computer Science, Univ. of Houston, Houston, TX;Computational Biomedicine Lab., Dept. of Computer Science, Univ. of Houston, Houston, TX;Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA;Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA;Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA;Computational Biomedicine Lab., Dept. of Computer Science, Univ. of Houston, Houston, TX

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

The diaphragm is a thin double-domed muscle that separates the thoracic and abdominal cavities. An accurate delineation of the diaphragm surface will be useful in providing a good region of interest for segmentation problems pertaining to the thoracic and abdominal cavities. In this paper, we present a fully automatic 3D graph-based method for the segmentation of the diaphragm in non-contrast CT data. In particular, we reformulate the diaphragm segmentation problem as an optimal surface segmentation problem in a volumetric graph. Comparison of the results obtained using our method with manual segmentations performed by an expert on non-contrast cardiac CT scans of 7 randomly selected patients indicated an overlap of 94.20 ± 0.01 %.