Improving parenchyma segmentation by simultaneous estimation of tissue property T 1 map and group-wise registration of inversion recovery MR breast images

Ye Xing, Zhong Xue, Sarah Englander, Mitchell Schnall, Dinggang Shen

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

5 Citations (Scopus)

Abstract

The parenchyma tissue in the breast has a strong relation with predictive biomarkers of breast cancer. To better segment parenchyma, we perform segmentation on estimated tissue property T 1 map. To improve the estimation of tissue property (T 1) which is the basis for parenchyma segmentation, we present an integrated algorithm for simultaneous T 1 map estimation, T 1 map based parenchyma segmentation and group-wise registration on series of inversion recovery magnetic resonance (MR) breast images. The advantage of using this integrated algorithm is that the simultaneous T 1 map estimation (E-step) and group-wise registration (R-step) could benefit each other and jointly improve parenchyma segmentation. In particular, in E-step, T 1 map based segmentation could help perform an edge-preserving smoothing on the tentatively estimated noisy T 1 map, and could also help provide tissue probability maps to be robustly registered in R-step. Meanwhile, the improved estimation of T 1 map could help segment parenchyma in a more accurate way. In R-step, for robust registration, the group-wise registration is performed on the tissue probability maps produced in E-step, rather than the original inversion recovery MR images, since tissue probability maps are the intrinsic tissue property which is invariant to the use of different imaging parameters. The better alignment of images achieved in R-step can help improve T 1 map estimation and indirectly the T 1 map based parenchyma segmentation. By iteratively performing E-step and R-step, we can simultaneously obtain better results for T 1 map estimation,T 1 map based segmentation, group-wise registration, and finally parenchyma segmentation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages342-350
Number of pages9
Volume5241 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States
Duration: 2008 Sep 62008 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5241 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
CountryUnited States
CityNew York, NY
Period08/9/608/9/10

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Xing, Y., Xue, Z., Englander, S., Schnall, M., & Shen, D. (2008). Improving parenchyma segmentation by simultaneous estimation of tissue property T 1 map and group-wise registration of inversion recovery MR breast images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5241 LNCS, pp. 342-350). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_41