title: Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans.
abstract: OBJECTIVE
  This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them.
  
  
  METHODS
  To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by two radiologists.
  
  
  RESULTS
  Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively.
  
  
  CONCLUSIONS
  The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer in thin-section CT images.
authors.affiliation: Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, Research Institute, National Cancer Center, Goyang, Korea.; Department of Plasma Bio Display, Kwangwoon University, Seoul, Korea.
authors.forename: Young Jae
authors.initials: YJ
authors.lastname: Kim
authors.affiliation: Department of Plasma Bio Display, Kwangwoon University, Seoul, Korea.
authors.forename: Seung Hyun
authors.initials: SH
authors.lastname: Lee
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul, Korea.
authors.forename: Chang Min
authors.initials: CM
authors.lastname: Park
authors.affiliation: Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, Research Institute, National Cancer Center, Goyang, Korea.
authors.forename: Kwang Gi
authors.initials: KG
authors.lastname: Kim
keywords: Computer-Assisted Diagnosis; Computer-Assisted Image Processing; Lung; Solitary Pulmonary Nodule; X-Ray Computed Tomography Scanners