Improved Segmentation of Low-Contrast Fibroglandular Structures in High-Noise Breast CT Volumes for XCAT Modeling
J Wells*, P Segars, J Dobbins, Duke University Medical Center, Durham, NCTH-A-103-10 Thursday 8:00AM - 9:55AM Room: 103
Purpose: This work improves the accuracy and realism of automated breast computed tomography (bCT) tissue segmentation by refining the detection of low-contrast fibroglandular structures to produce high-resolution realistic computer-generated (XCAT) breast phantoms from empirical human subject data.
Methods: Previous work by Hsu et al. [Med. Phys. 38, 5756-5770 (2011)] produced high-resolution realistic computer-generated breast phantoms from empirical human subject data but challenges were encountered with the accurate segmentation of fine, low-contrast glandular structures. The current work addresses those challenges. A 3-D anisotropic diffusion algorithm was used to denoise fourteen bCT datasets. After breast masking, two adipose non-uniformity correction techniques were applied. The first has been described by Altunbas, et al. [Med. Phys. 34, 3109-3118 (2007)]. The second approach employed an original technique using higher-order polynomials to correct for residual adipose non-uniformity. Histogram thresholding then produced initial gland and skin segmentations. This was followed by a novel glandular linking and extension protocol based on skeletonization of the skin and glandular segmentations and a pixel gray-level-weighted distance transform. Skin mask definition and glandular density differentiation completed the segmentation.
Results: Volumetric denoising reduced the standard deviation of the adipose background by an average of 60.4%. The Altunbas method corrected for radially symmetric, quadratic non-uniformities in breasts with circular coronal cross sections, but performed poorly on high-density breasts and breasts with asymmetric adipose non-uniformity. Follow-up correction using the novel method improved adipose uniformity by an average of 24.6%. The new fibroglandular linking and extension protocol improved the detection of low-contrast fibroglandular structures, including Cooper's ligaments. The total number of fibroglandular tissue islands was also reduced.
Conclusion: The semi-automated bCT segmentation protocol improved low-contrast glandular fiber detection in high-noise reconstructions. Linking of disparate fibroglandular tissue islands and capture of Cooper's ligaments will contribute to the overall accuracy and realism of empirically-derived XCAT breast phantoms.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by NIH Grant 5R01-CA-134658.