title: Detection of masses in digital breast tomosynthesis using complementary information of simulated projection.
abstract: OBJECTIVE
  The purpose of this study is to develop a computer-aided detection system that combines the detection results in 3D digital breast tomosynthesis (DBT) volume and 2D simulated projection (synthesized image which is not provided by the vendor but generated from DBT volume in this study) to improve the accuracy of mass detection in DBT.
  
  
  METHODS
  The 3D DBT volume has a problem of blurring in the out-of-focus plane because it is reconstructed from a limited number of projection view images acquired over a limited angular range. To solve the problem, the simulated projection is generated by measuring the blurriness of voxels in the DBT volume and adopting conspicuity voxels. A contour-based detection algorithm is applied to detecting masses in the simulated projection. The DBT volume is analyzed by using an unsupervised mass detection algorithm, which results in mass candidates in the DBT volume. The mass likelihood scores estimated for mass candidates on the DBT volume and the simulated projection are merged in a probabilistic manner through a Bayesian network model to differentiate masses and false positives (FPs). Experiments were conducted on a clinical data set of 320 DBT volumes. In 90 volumes, at least one biopsy-proven malignant mass was presented. The longest diameter of masses ranged from 7.0 to 56.4 mm (mean = 25.4 mm). The sizes of masses in the data set were relatively large compared to the sizes of the masses reported in other detection studies. Three image quality measurements (overall sharpness, sharpness of mass boundary, and contrast) were used to evaluate the image quality of the simulated projection compared to the DBT central slice where the mass was most conspicuous and other projection methods (maximum intensity projection and average projection). A free-response receiver operating characteristic (FROC) analysis was adopted for evaluating the accuracy of mass detection in the DBT volume, the simulated projection, and the combined approach. A jackknife FROC analysis (JAFROC) was used to estimate the statistical significance of the difference between two FROC curves.
  
  
  RESULTS
  The overall sharpness and the sharpness of mass boundary in the simulated projection are higher than those in the DBT central slice and other projection methods. The contrast of the simulated projection is lower than the DBT central slice. The mass detection in the DBT volume achieved region-based sensitivities of 80% and 85% with 1.75 and 2.11 FPs per DBT volume. The proposed combined mass detection approach achieved same sensitivities with reduced FPs of 1.33 and 1.93 per DBT volume. The difference of the FROC curves between the combined approach and the mass detection in the DBT volume was statistically significant (p < 0.01) by JAFROC analysis.
  
  
  CONCLUSIONS
  This study indicates that the combined approach that merges the detection results in the DBT volume and the simulated projection is a promising approach to improve the accuracy of mass detection in DBT.
authors.affiliation: Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-Gu, Daejeon 305-701, Republic of Korea.
authors.forename: Seong Tae
authors.initials: ST
authors.lastname: Kim
authors.affiliation: Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-Gu, Daejeon 305-701, Republic of Korea.
authors.forename: Dae Hoe
authors.initials: DH
authors.lastname: Kim
authors.affiliation: Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-Gu, Daejeon 305-701, Republic of Korea.
authors.forename: Yong Man
authors.initials: YM
authors.lastname: Ro
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000601:surgery; D003198:Computer Simulation; D066264:Datasets as Topic; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D008327:Mammography / Q000379:methods*; D012372:ROC Curve