Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria

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

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.

Original languageEnglish (US)
Pages (from-to)237-247
Number of pages11
JournalJournal of Digital Imaging
Volume27
Issue number2
DOIs
StatePublished - 2014

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Breast
Tissue
Chemical analysis

Keywords

  • 3D segmentation
  • Breast
  • Computer-aided diagnosis (CAD)
  • Dedicated breast CT

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Computer Science Applications

Cite this

Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria. / Kuo, Hsien Chi; Giger, Maryellen L.; Reiser, Ingrid; Boone, John M; Lindfors, Karen K; Yang, Kai; Edwards, Alexandra.

In: Journal of Digital Imaging, Vol. 27, No. 2, 2014, p. 237-247.

Research output: Contribution to journalArticle

Kuo, Hsien Chi ; Giger, Maryellen L. ; Reiser, Ingrid ; Boone, John M ; Lindfors, Karen K ; Yang, Kai ; Edwards, Alexandra. / Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria. In: Journal of Digital Imaging. 2014 ; Vol. 27, No. 2. pp. 237-247.
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