title: Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT.
abstract: OBJECTIVE
  This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images.
  
  
  METHODS
  In order to track the fuzzy liver-chestwall and liver-heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors' automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist.
  
  
  RESULTS
  The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm(3), whereas it was 1719 cm(3) for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98.
  
  
  CONCLUSIONS
  The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors' results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.
authors.affiliation: Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Yading
authors.initials: Y
authors.lastname: Yuan
authors.affiliation: Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Ming
authors.initials: M
authors.lastname: Chao
authors.affiliation: Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Ren-Dih
authors.initials: RD
authors.lastname: Sheu
authors.affiliation: Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rosenzweig
authors.affiliation: Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
authors.forename: Yeh-Chi
authors.initials: YC
authors.lastname: Lo
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging* / Q000601:surgery; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D016634:Radiosurgery / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*