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 language | English (US) |
|---|---|
| Article number | 014501 |
| Journal | Journal of Medical Imaging |
| Volume | 1 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 1 2014 |
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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
Cite this
Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images. / Kuo, Hsien Chi; Giger, Maryellen L.; Reiser, Ingrid; Drukker, Karen; Boone, John M; Lindfors, Karen K; Yang, Kai; Edwards, Alexandra; Sennett, Charlene A.
In: Journal of Medical Imaging, Vol. 1, No. 1, 014501, 01.04.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images
AU - Kuo, Hsien Chi
AU - Giger, Maryellen L.
AU - Reiser, Ingrid
AU - Drukker, Karen
AU - Boone, John M
AU - Lindfors, Karen K
AU - Yang, Kai
AU - Edwards, Alexandra
AU - Sennett, Charlene A.
PY - 2014/4/1
Y1 - 2014/4/1
N2 - 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.
AB - 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.
KW - active contour model
KW - breast computed tomography
KW - computer-aided diagnosis
KW - image analysis
KW - segmentation
KW - three-dimensional automated breast ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85015395940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015395940&partnerID=8YFLogxK
U2 - 10.1117/1.JMI.1.1.014501
DO - 10.1117/1.JMI.1.1.014501
M3 - Article
AN - SCOPUS:85015395940
VL - 1
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
SN - 0720-048X
IS - 1
M1 - 014501
ER -