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

Hsien Chi Kuo, Maryellen L. Giger, Ingrid Reiser, Karen Drukker, John M Boone, Karen K Lindfors, Kai Yang, Alexandra Edwards, Charlene A. Sennett

Research output: Contribution to journalArticle

15 Scopus citations

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.

Original languageEnglish (US)
Article number014501
JournalJournal of Medical Imaging
Volume1
Issue number1
DOIs
StatePublished - Apr 1 2014

Keywords

  • active contour model
  • breast computed tomography
  • computer-aided diagnosis
  • image analysis
  • segmentation
  • three-dimensional automated breast ultrasound

ASJC Scopus subject areas

  • Bioengineering
  • Radiology Nuclear Medicine and imaging

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