Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images

Abstract. We present and evaluate a method for the three-dimensional (3-D) segmentation of breast masses on dedicated breast computed tomography (bCT) and automated 3-D breast ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D breast ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant (p=0.002). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.

[1]  J. Boone,et al.  Dedicated breast CT: initial clinical experience. , 2008, Radiology.

[2]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[3]  Maryellen L. Giger,et al.  Automated seeded lesion segmentation on digital mammograms , 1998, IEEE Transactions on Medical Imaging.

[4]  Yongjian Yu,et al.  Segmentation of the prostate from suprapubic ultrasound images. , 2004, Medical physics.

[5]  Maryellen L. Giger,et al.  Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria , 2014, Journal of Digital Imaging.

[6]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  John J Heine,et al.  Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements. , 2011, Academic radiology.

[8]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

[9]  Robert M. Nishikawa,et al.  Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images , 2010, Medical Imaging.

[10]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[11]  C. D'Orsi,et al.  Dedicated breast computed tomography: the optimal cross-sectional imaging solution? , 2010, Radiologic clinics of North America.

[12]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[13]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[14]  P. Narula.  MAMMOGRAPHIC DENSITY AND THE RISK AND DETECTION OF BREAST CANCER , 2016 .

[15]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[16]  V. McCormack,et al.  Breast Cancer Risk : A Meta-analysis Breast Density and Parenchymal Patterns as Markers of Updated , 2006 .

[17]  John M Boone,et al.  Contrast-enhanced dedicated breast CT: initial clinical experience. , 2010, Radiology.

[18]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[19]  Matei Mancas,et al.  Fuzzy Tumor Segmentation based on Iterative Watersheds , 2003 .

[20]  P. Huynh,et al.  The false-negative mammogram. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[21]  Stephen J Glick,et al.  Breast CT. , 2007, Annual review of biomedical engineering.

[22]  Kenji Suzuki,et al.  A dual-stage method for lesion segmentation on digital mammograms. , 2007, Medical physics.

[23]  J. March.  Introduction to the Calculus of Variations , 1999 .

[24]  M J Yaffe,et al.  The myth of the 50-50 breast. , 2009, Medical physics.

[25]  Max A. Viergever,et al.  A discrete dynamic contour model , 1995, IEEE Trans. Medical Imaging.

[26]  M L Giger,et al.  Automated detection of mass lesions in dedicated breast CT: a preliminary study. , 2012, Medical physics.

[27]  Jianwu Xu,et al.  Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. , 2010, Medical physics.

[28]  Berkman Sahiner,et al.  Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study. , 2011, Radiology.

[29]  C. D'Orsi,et al.  Mammographic feature analysis. , 1993, Seminars in roentgenology.

[30]  T. M. Kolb,et al.  Occult cancer in women with dense breasts: detection with screening US--diagnostic yield and tumor characteristics. , 1998, Radiology.

[31]  Jiayu Chen,et al.  Automated Full-field Breast Ultrasonography: The Past and The Present , 2007 .

[32]  Karen Drukker,et al.  Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts. , 2013, Academic radiology.

[33]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[34]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[35]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[36]  Xiao Han,et al.  Geometric Deformable Models , 2015 .

[37]  J. Wolfe.  Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[38]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[39]  Maryellen L. Giger,et al.  Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT , 2012, Digital Mammography / IWDM.

[40]  John M. Boone,et al.  Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features , 2012, Medical Imaging.

[41]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[42]  Michaela C. C. Weigel,et al.  High-resolution spiral CT of the breast at very low dose: concept and feasibility considerations , 2011, European Radiology.

[43]  Ruey-Feng Chang,et al.  3-D breast ultrasound segmentation using active contour model. , 2003, Ultrasound in medicine & biology.

[44]  Ruey-Feng Chang,et al.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors , 2004, Breast Cancer Research and Treatment.

[45]  Richard W. Hamming,et al.  Numerical Methods for Scientists and Engineers , 1963 .