3D segmentation of breast masses from Digital Breast Tomosynthesis images

Stefanie Pöhlmann, Yit Lim, Elaine Harkness, S Pritchard, Christopher Taylor, Susan Astley

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Assessment of three-dimensional morphology and volume of breast masses is important for cancer diagnosis, staging and treatment, but cannot be derived from conventional mammography. Digital Breast Tomosynthesis (DBT) provides data from which three-dimensional mass segmentation could be obtained. Our method combined Gaussian mixture models based on intensity and a texture measure indicative of in-focus structure, gray level variance. Thresholding these voxel probabilities, weighted by distance to the estimated mass center, gave the final three-dimensional segmentation. Evaluation used 40 masses annotated twice by a consultant radiologist on in-focus slices in two diagnostic views. Human intra-observer variability was assessed as the overlap between repeated annotations (median 77%, range 25–91%). Comparing the segmented mass outline with probability-weighted ground truth from these annotations, median agreement was 68%, range 7–88%. Annotated and segmented diameters correlated well with histological mass size (both Spearman’s rank correlations ρ=0.69). The volumetric segmentation demonstrated better agreement with tumor volumes estimated from pathology than volume derived from radiological annotations (95% limits of agreement -16 to 11 ml and -23 to 41 ml, respectively). We conclude that it is feasible to assess three-dimensional mass morphology and volume from DBT, and the method has the potential to aid breast cancer management.
Original languageEnglish
JournalJournal of Medical Imaging
Publication statusPublished - 19 Sept 2017


  • Digital breast tomosynthesis (DBT);
  • Mass Segmentation
  • Tumor size
  • tumor volume
  • Gaussian mixture modeling
  • Texture


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