Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at http://www.ucl.ac.uk/finance/research/post_award/post_award_contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Doran SJ, Hipwell JH, Denholm R, Eiben B, Busana M, Hawkes DJ, Leach MO, Dos Santos Silva I
  • Publication date:
  • Journal:
    Medical physics
  • Medium:
  • Print ISSN:
  • Language:
  • Addresses:
    Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, SM2 5NG, UK.
To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection.Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semi-automated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from inphase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analysed using a novel and fully automated algorithm involving image filtering, landmark identification and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximisation - Markov Random Field technique, yielding a second independent estimate of MRI density.Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and inter-class correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue.Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient. This article is protected by copyright. All rights reserved.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Dept of Med Phys & Biomedical Eng
Dept of Med Phys & Biomedical Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by