Simultaneous estimation and segmentation of T 1 map for breast parenchyma measurement

Xing Ye; Ou Yangming; Sarah Englander; Mitchell Schnall; Shen Dinggang

doi:10.1109/ISBI.2007.356856

Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement

Xing Ye, Ou Yangming, Sarah Englander, Mitchell Schnall, Shen Dinggang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T ₁ map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T ₁ map estimation (E-Step) and the step of T ₁ map based tissue segmentation (S-Step) can benefit each other. Since the estimated T ₁ map can be noisy due to the complexity of T ₁ estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T ₁ map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T ₁ map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T ₁ map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.

Original language	English
Title of host publication	2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro - Proceedings
Pages	332-335
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2007.356856
Publication status	Published - 2007
Externally published	Yes
Event	2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States Duration: 2007 Apr 12 → 2007 Apr 15

Publication series

Name	2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Other

Other	2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country	United States
City	Arlington, VA
Period	07/4/12 → 07/4/15

Keywords

Breast cancer
Estimation
Image segmentation
Parenchyma
T map

ASJC Scopus subject areas

Electrical and Electronic Engineering
Medicine(all)

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.1109/ISBI.2007.356856

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Cite this

Ye, X., Yangming, O., Englander, S., Schnall, M., & Dinggang, S. (2007). Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (pp. 332-335). [4193290] (2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings). https://doi.org/10.1109/ISBI.2007.356856

Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement. / Ye, Xing; Yangming, Ou; Englander, Sarah; Schnall, Mitchell; Dinggang, Shen.

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2007. p. 332-335 4193290 (2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, X, Yangming, O, Englander, S, Schnall, M & Dinggang, S 2007, Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement. in 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings., 4193290, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings, pp. 332-335, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07, Arlington, VA, United States, 07/4/12. https://doi.org/10.1109/ISBI.2007.356856

Ye X, Yangming O, Englander S, Schnall M, Dinggang S. Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2007. p. 332-335. 4193290. (2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings). https://doi.org/10.1109/ISBI.2007.356856

Ye, Xing ; Yangming, Ou ; Englander, Sarah ; Schnall, Mitchell ; Dinggang, Shen. / Simultaneous estimation and segmentation of T ₁ map for breast parenchyma measurement. 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2007. pp. 332-335 (2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings).

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abstract = "Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.",

keywords = "Breast cancer, Estimation, Image segmentation, Parenchyma, T map",

author = "Xing Ye and Ou Yangming and Sarah Englander and Mitchell Schnall and Shen Dinggang",

note = "Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 ; Conference date: 12-04-2007 Through 15-04-2007",

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N2 - Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.

AB - Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.

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