19 September 2017 Three-dimensional segmentation of breast masses from digital breast tomosynthesis images
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Abstract
Assessment of three-dimensional (3-D) 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 3-D 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 3-D segmentation. Evaluation used 40 masses annotated twice by a consultant radiologist on in-focus slices in two diagnostic views. Human intraobserver variability was assessed as the overlap between repeated annotations (median 77% and range 25% to 91%). Comparing the segmented mass outline with probability-weighted ground truth from these annotations, median agreement was 68%, and range was 7% to 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 3-D mass morphology and volume from DBT, and the method has the potential to aid breast cancer management.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Stefanie T. L. Pöhlmann, Stefanie T. L. Pöhlmann, Yit Y. Lim, Yit Y. Lim, Elaine Harkness, Elaine Harkness, Susan Pritchard, Susan Pritchard, Christopher J. Taylor, Christopher J. Taylor, Susan M. Astley, Susan M. Astley, } "Three-dimensional segmentation of breast masses from digital breast tomosynthesis images," Journal of Medical Imaging 4(3), 034007 (19 September 2017). https://doi.org/10.1117/1.JMI.4.3.034007 . Submission: Received: 31 January 2017; Accepted: 16 August 2017
Received: 31 January 2017; Accepted: 16 August 2017; Published: 19 September 2017
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