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Breast cancer, MRI, FCM

Harini Veeraraghavan, Brittany Z Dashevsky, Natsuko Onishi, Meredith Sadinski, Elizabeth Morris, Joseph O Deasy, Elizabeth J Sutton
We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR)...
March 19, 2018: Scientific Reports
Woo Kyung Moon, Jie-Fan Chang, Chung-Ming Lo, Jung Min Chang, Su Hyun Lee, Sung Ui Shin, Chiun-Sheng Huang, Ruey-Feng Chang
BACKGROUND AND OBJECTIVE: Breast density at mammography has been used as markers of breast cancer risk. However, newly introduced tomosynthesis and computer-aided quantitative method could provide more reliable breast density evaluation. METHODS: In the experiment, 98 tomosynthesis image volumes were obtained from 98 women. For each case, an automatic skin removal was used and followed by a fuzzy c-mean (FCM) classifier which separated the fibroglandular tissues from other tissues in breast area...
February 2018: Computer Methods and Programs in Biomedicine
Huanjun Ding, Travis Johnson, Muqing Lin, Huy Q Le, Justin L Ducote, Min-Ying Su, Sabee Molloi
PURPOSE: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. METHODS: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner...
December 2013: Medical Physics
Shandong Wu, Susan P Weinstein, Emily F Conant, Despina Kontos
PURPOSE: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment...
December 2013: Medical Physics
Yi Wang, Glen Morrell, Marta E Heibrun, Allison Payne, Dennis L Parker
RATIONALE AND OBJECTIVES: The goal of the study is to develop a technique to achieve accurate volumetric breast tissue segmentation using magnetic resonance imaging (MRI) data. This segmentation can be useful to aid in the diagnosis of breast cancers and to assess breast cancer risk based on breast density. Tissue segmentation is also essential for development of acoustic and thermal models used in magnetic resonance guided high-intensity focused ultrasound treatment of breast lesions...
February 2013: Academic Radiology
Ahmed B Ashraf, Sara C Gavenonis, Dania Daye, Carolyn Mies, Mark A Rosen, Despina Kontos
We present a methodological framework for multichannel Markov random fields (MRFs). We show that conditional independence allows loopy belief propagation to solve a multichannel MRF as a single channel MRF. We use conditional mutual information to search for features that satisfy conditional independence assumptions. Using this framework we incorporate kinetic feature maps derived from breast dynamic contrast enhanced magnetic resonance imaging as observation channels in MRF for tumor segmentation. Our algorithm based on multichannel MRF achieves an receiver operating characteristic area under curve (AUC) of 0...
April 2013: IEEE Transactions on Medical Imaging
Ahmed B Ashraf, Sara Gavenonis, Dania Daye, Carolyn Mies, Michael Feldman, Mark Rosen, Despina Kontos
We present a multichannel extension of Markov random fields (MRFs) for incorporating multiple feature streams in the MRF model. We prove that for making inference queries, any multichannel MRF can be reduced to a single channel MRF provided features in different channels are conditionally independent given the hidden variable, Using this result we incorporate kinetic feature maps derived from breast DCE MRI into the observation model of MRF for tumor segmentation. Our algorithm achieves an ROC AUC of 0.97 for tumor segmentation, We present a comparison against the commonly used approach of fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES)...
2011: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Muqing Lin, Siwa Chan, Jeon-Hor Chen, Daniel Chang, Ke Nie, Shih-Ting Chen, Cheng-Ju Lin, Tzu-Ching Shih, Orhan Nalcioglu, Min-Ying Su
PURPOSE: Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work...
January 2011: Medical Physics
Yanming Yu, Quan Jiang, Yanwei Miao, Jun Li, Shanglian Bao, Haoyu Wang, Chunxue Wu, Xiaoying Wang, Jiong Zhu, Yi Zhong, E Mark Haacke, Jiani Hu
PURPOSE: To develop a method that combines a fixed-T1, fuzzy c-means (FCM) technique with a reference region (RR) model (T1-FCM method) to estimate pharmacokinetic parameters without measuring the arterial input function or baseline T1, or T1(0), and to demonstrate its feasibility in the assessment of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer by using data from dynamic contrast material-enhanced magnetic resonance (MR) imaging. MATERIALS AND METHODS: This study was approved by the human investigation committees of the two participating institutions...
October 2010: Radiology
Jun Li, Yanming Yu, Yibao Zhang, Shanglian Bao, Chunxue Wu, Xiaoying Wang, Jie Li, Xiaopeng Zhang, Jiani Hu
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the MRI technique of choice for detecting breast cancer, which can be roughly classified as either quantitative or semiquantitative. The major advantage of quantitative DCE-MRI is its ability to provide pharmacokinetic parameters such as volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve). However, semiquantitative DCE-MRI is still the clinical MRI technique of choice for breast cancer diagnosis due to several major practical difficulties in the implementation of quantitative DCE-MRI in a clinical setting, including (1) long acquisition necessary to acquire 3D T1(0) map, (2) challenges in obtaining accurate artery input function (AIF), (3) long computation time required by conventional nonlinear least square (NLS) fitting, and (4) many illogical values often generated by conventional NLS method...
August 2009: Medical Physics
Ke Nie, Jeon-Hor Chen, Siwa Chan, Man-Kwun I Chau, Hon J Yu, Shadfar Bahri, Tiffany Tseng, Orhan Nalcioglu, Min-Ying Su
Breast density has been established as an independent risk factor associated with the development of breast cancer. It is known that an increase of mammographic density is associated with an increased cancer risk. Since a mammogram is a projection image, different body position, level of compression, and the x-ray intensity may lead to a large variability in the density measurement. Breast MRI provides strong soft tissue contrast between fibroglandular and fatty tissues, and three-dimensional coverage of the entire breast, thus making it suitable for density analysis...
December 2008: Medical Physics
C Klifa, J Carballido-Gamio, L Wilmes, A Laprie, C Lobo, E Demicco, M Watkins, J Shepherd, J Gibbs, N Hylton
The study objective was to develop a segmentation technique to quantify breast tissue and total breast volume from magnetic resonance imaging (MRI) data to obtain a breast tissue index (BTI) related to breast density. Our goal is to quantify MR breast density to improve breast cancer risk assessment for certain high-risk populations for whom mammography is of limited usefulness due to high breast density. A semi-automatic 3D segmentation technique was implemented based on a fuzzy c-means technique (FCM) to segment fibroglandular tissue from fat in the breast images...
2004: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Weijie Chen, Maryellen L Giger, Ulrich Bick, Gillian M Newstead
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions...
August 2006: Medical Physics
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