Literature DB >> 23292922

Comparison of 3-point Dixon imaging and fuzzy C-means clustering methods for breast density measurement.

Tess V Clendenen1, Anne Zeleniuch-Jacquotte, Linda Moy, Malcolm C Pike, Henry Rusinek, Sungheon Kim.   

Abstract

PURPOSE: To assess two methods of fat and fibroglandular tissue (FGT) segmentation for measuring breast MRI FGT volume and FGT%, the volume percentage of FGT in the breast, in longitudinal studies.
MATERIALS AND METHODS: Nine premenopausal women provided one MRI per week for 4 weeks during a natural menstrual cycle for a total of 36 datasets. We compared a fuzzy c-means (FC) and a 3-point Dixon segmentation method for estimation of changes in FGT volume and FGT% across the menstrual cycle. We also assessed whether differences due to changes in positioning each week could be minimized by coregistration, i.e., the application of the breast boundary selected at one visit to images obtained at other visits.
RESULTS: FC and Dixon FGT volume were highly correlated (r = 0.93, P < 0.001), as was FC and Dixon FGT% (r = 0.86, P = 0.01), although Dixon measurements were on average 10-20% higher. Although FGT measured by both methods showed the expected pattern of increase during the menstrual cycle, the magnitude, and for one woman the direction, of change varied according to the method used. Measurements of FGT for coregistered images were in close agreement with those for which the boundaries were determined independently.
CONCLUSION: The method of segmentation of fat and FGT tissue may have an impact on the results of longitudinal studies of changes in breast MRI FGT.
Copyright © 2012 Wiley Periodicals, Inc.

Entities:  

Keywords:  3-point Dixon; breast MRI; fibroglandular tissue; fibroglandular tissue %, breast density; fuzzy c-means; segmentation

Mesh:

Year:  2013        PMID: 23292922     DOI: 10.1002/jmri.24002

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  16 in total

1.  Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Authors:  Shandong Wu; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

2.  Breast density estimation from high spectral and spatial resolution MRI.

Authors:  Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-28

3.  Reliability of supraspinatus intramuscular fatty infiltration estimates on T1-weighted MRI in potential candidates for rotator cuff repair surgery: full-thickness tear versus high-grade partial-thickness tear.

Authors:  Derik L Davis; Mohit N Gilotra; Rodolfo Calderon; Andrew Roberts; S Ashfaq Hasan
Journal:  Skeletal Radiol       Date:  2021-05-06       Impact factor: 2.199

4.  Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

Authors:  Richard Ha; Peter Chang; Eralda Mema; Simukayi Mutasa; Jenika Karcich; Ralph T Wynn; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

5.  Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Authors:  Yang Zhang; Jeon-Hor Chen; Kai-Ting Chang; Vivian Youngjean Park; Min Jung Kim; Siwa Chan; Peter Chang; Daniel Chow; Alex Luk; Tiffany Kwong; Min-Ying Su
Journal:  Acad Radiol       Date:  2019-01-31       Impact factor: 3.173

6.  Automated breast segmentation of fat and water MR images using dynamic programming.

Authors:  José A Rosado-Toro; Tomoe Barr; Jean-Philippe Galons; Marilyn T Marron; Alison Stopeck; Cynthia Thomson; Patricia Thompson; Danielle Carroll; Eszter Wolf; María I Altbach; Jeffrey J Rodríguez
Journal:  Acad Radiol       Date:  2015-02       Impact factor: 3.173

Review 7.  Background parenchymal enhancement on breast MRI: A comprehensive review.

Authors:  Geraldine J Liao; Leah C Henze Bancroft; Roberta M Strigel; Rhea D Chitalia; Despina Kontos; Linda Moy; Savannah C Partridge; Habib Rahbar
Journal:  J Magn Reson Imaging       Date:  2019-04-19       Impact factor: 4.813

8.  Dixon imaging-based partial volume correction improves quantification of choline detected by breast 3D-MRSI.

Authors:  Lenka Minarikova; Stephan Gruber; Wolfgang Bogner; Katja Pinker-Domenig; Pascal A T Baltzer; Thomas H Helbich; Siegfried Trattnig; Marek Chmelik
Journal:  Eur Radiol       Date:  2014-09-14       Impact factor: 5.315

9.  MRI assessment of the thigh musculature in dermatomyositis and healthy subjects using diffusion tensor imaging, intravoxel incoherent motion and dynamic DTI.

Authors:  E E Sigmund; S H Baete; T Luo; K Patel; D Wang; I Rossi; A Duarte; M Bruno; D Mossa; A Femia; S Ramachandran; D Stoffel; J S Babb; A G Franks; J Bencardino
Journal:  Eur Radiol       Date:  2018-06-04       Impact factor: 5.315

10.  Quantification of shoulder muscle intramuscular fatty infiltration on T1-weighted MRI: a viable alternative to the Goutallier classification system.

Authors:  Derik L Davis; Thomas Kesler; Mohit N Gilotra; Ranyah Almardawi; Syed A Hasan; Rao P Gullapalli; Jiachen Zhuo
Journal:  Skeletal Radiol       Date:  2018-09-10       Impact factor: 2.199

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