Level set breast mass segmentation in contrast-enhanced and non-contrast-enhanced breast CT

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Dedicated breast CT (bCT) is an emerging technology that produces 3D reconstructed images of the breast, thus allowing radiologists to detect and evaluate breast lesions in 3D. In previous work, we have developed an algorithm that combines radial gradient index (RGI) segmentation and a modified level set model for segmentation of lesions in contrast-enhanced bCT images; yielding an average overlap ratio (OR) of 0.69, which is higher than 0.4, the overlap ratio that is generally deemed "acceptable". In this study, this segmentation algorithm, with the same parameter settings, was applied to the corresponding non-contrastenhanced bCT images. The results show that the OR obtained on non-contrast images was 0.62, with the segmented lesion volumes tending to be slightly smaller as compared with those obtained on the corresponding contrast-enhanced images. These results imply that while use of contrast improves segmentation performance, the increase may not be significant, and thus, the role of noncontrast-enhanced breast CT should be further investigated.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages697-704
Number of pages8
Volume7361 LNCS
DOIs
StatePublished - 2012
Event11th International Workshop on Breast Imaging, IWDM 2012 - Philadelphia, PA, United States
Duration: Jul 8 2012Jul 11 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7361 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Workshop on Breast Imaging, IWDM 2012
CountryUnited States
CityPhiladelphia, PA
Period7/8/127/11/12

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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    Kuo, H. C., Giger, M. L., Reiser, I., Boone, J. M., Lindfors, K. K., Yang, K., & Edwards, A. (2012). Level set breast mass segmentation in contrast-enhanced and non-contrast-enhanced breast CT. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7361 LNCS, pp. 697-704). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7361 LNCS). https://doi.org/10.1007/978-3-642-31271-7_90