title: Semiautomatic segmentation of liver metastases on volumetric CT images.
abstract: OBJECTIVE
  Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images.
  
  
  METHODS
  The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1-10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the "gold standard" for validation of the method's accuracy.
  
  
  RESULTS
  The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm.
  
  
  CONCLUSIONS
  Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation method.
authors.affiliation: Department of Biomedical Engineering, Shanghai University of Medicine & Health Sciences, 101 Yingkou Road, Yang Pu District, Shanghai 200093, China.
authors.forename: Jiayong
authors.initials: J
authors.lastname: Yan
authors.affiliation: Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
authors.forename: Lawrence H
authors.initials: LH
authors.lastname: Schwartz
authors.affiliation: Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
authors.forename: Binsheng
authors.initials: B
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*