title: Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.
abstract: OBJECTIVE
  The purpose of this study is to reveal how the performance of lung nodule segmentation algorithm impacts the performance of lung nodule detection, and to provide guidelines for choosing an appropriate segmentation algorithm with appropriate parameters in a computer-aided detection (CAD) scheme.
  
  
  METHODS
  The database consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter from the standard CT lung nodule database created by the Lung Image Database Consortium. The initial nodule candidates were identified as those with strong response to a selective nodule enhancement filter. A uniform viewpoint reformation technique was applied to a three-dimensional nodule candidate to generate 24 two-dimensional (2D) reformatted images, which would be used to effectively distinguish between true nodules and false positives. Six different algorithms were employed to segment the initial nodule candidates in the 2D reformatted images. Finally, 2D features from the segmented areas in the 24 reformatted images were determined, selected, and classified for removal of false positives. Therefore, there were six similar CAD schemes, in which only the segmentation algorithms were different. The six segmentation algorithms included the fixed thresholding (FT), Otsu thresholding (OTSU), fuzzy C-means (FCM), Gaussian mixture model (GMM), Chan and Vese model (CV), and local binary fitting (LBF). The mean Jaccard index and the mean absolute distance (Dmean) were employed to evaluate the performance of segmentation algorithms, and the number of false positives at a fixed sensitivity was employed to evaluate the performance of the CAD schemes.
  
  
  RESULTS
  For the segmentation algorithms of FT, OTSU, FCM, GMM, CV, and LBF, the highest mean Jaccard index between the segmented nodule and the ground truth were 0.601, 0.586, 0.588, 0.563, 0.543, and 0.553, respectively, and the corresponding Dmean were 1.74, 1.80, 2.32, 2.80, 3.48, and 3.18 pixels, respectively. With these segmentation results of the six segmentation algorithms, the six CAD schemes reported 4.4, 8.8, 3.4, 9.2, 13.6, and 10.4 false positives per CT scan at a sensitivity of 80%.
  
  
  CONCLUSIONS
  When multiple algorithms are available for segmenting nodule candidates in a CAD scheme, the "optimal" segmentation algorithm did not necessarily lead to the "optimal" CAD detection performance.
authors.affiliation: School of Computer, Shenyang Aerospace University, Daoyi Development District, Shenyang, Liaoning 110136, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Guo
authors.affiliation: Medical Imaging Information Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Pudong District, Shanghai 201203, China and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D016013:Likelihood Functions; D016011:Normal Distribution; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*