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Automated Analysis of Breast Tumour in the Breast DCE-MR Images Using Level Set Method and Selective Enhancement of Invasive Regions

  • Atsushi Teramoto
  • Satomi Miyajo
  • Hiroshi Fujita
  • Osamu Yamamuro
  • Kumiko Omi
  • Masami Nishio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Analysis of invasive regions using breast magnetic resonance (MR) images plays an important role in diagnosis and decision-making regarding the treatment method. However, many images are obtained by MR imaging (MRI); development of an automated analysis method for breast tumours is desired. The main purpose of this study was to develop a novel method for automated analysis of the tumour region in breast MR images. First, early and late-subtraction images were obtained by subtracting early- and late-contrast-enhanced MR images, respectively, from the pre-contrast ones. Then, tumours in the images were enhanced based on the signal values of the normal mammary regions. Subsequently, using the level set method, a type of dynamic contour extraction, the outline of the tumour in the tumour-enhanced images was obtained. In order to evaluate the usefulness of the analysis method, we compared the tumour size listed in the interpretation report by a physician and analyzed the results obtained from the proposed method using clinical images from 10 cases. The mean absolute error of the size of tumours in all cases was less than 3.0 mm. These results indicate that the proposed method may be useful for the automated analysis of invasive breast tumours using breast MR images.

Keywords

Breast tumour Analysis Magnetic resonance imaging Level set 

Notes

Acknowledgment

The authors are grateful to Toshiki Kobayashi, Tsuneo Tamaki, and Masami Nishio of the Nagoya Radiological Diagnosis Foundation. This research was supported in part by a Grant-in-Aid for Scientific Research on Innovative Areas (#26108005), MEXT, Japan.

References

  1. 1.
    Breast Cancer Statistics, World Cancer Research Fund International. http://www.wcrf.org/int/cancer-facts-figures/data-specific-cancers/breast-cancer-statistics
  2. 2.
    Kuhl, C.: The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 244(2), 356–378 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Mann, R.M., Kuhl, C.K., Kinkel, K., et al.: Breast MRI: guidelines from the European society of breast imaging. Eur. Radiol. 18, 1307–1318 (2008)CrossRefGoogle Scholar
  4. 4.
    ACR practice guideline for the performance of contrast-enhanced magnetic resonance imaging (MRI) of the breast. American college of radiology. http://www.acr.org/~/media/ACR/Documents/PGTS/guidelines/MRI_Breast.pdf. Accessed 12 Sept 2015
  5. 5.
    Chen, W., Giger, M.L., Bick, U., Newstead, G.M.: Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med. Phys. 33(8), 2878–2887 (2006)CrossRefGoogle Scholar
  6. 6.
    Bhooshan, N., Giger, M.L., Jansen, S.A., Li, H., Lan, L., Newstead, G.M.: Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 254(3), 680–690 (2010)CrossRefGoogle Scholar
  7. 7.
    Dorrius, M.D., Weide, M.C., Ooijen, P.M., Pijnappel, R.M., Oudkerk, M.: Computer-aided detection in breast MRI: a systematic review and meta-analysis. Eur. Radiol. 21(8), 1600–1608 (2011)CrossRefGoogle Scholar
  8. 8.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)CrossRefGoogle Scholar
  9. 9.
    ITK SNAP. http://www.itksnap.org/pmwiki/pmwiki.php. Accessed 12 Sept 2015

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Atsushi Teramoto
    • 1
  • Satomi Miyajo
    • 2
  • Hiroshi Fujita
    • 3
  • Osamu Yamamuro
    • 2
  • Kumiko Omi
    • 2
  • Masami Nishio
    • 4
  1. 1.School of Health SciencesFujita Health UniversityAichiJapan
  2. 2.East Nagoya Imaging Diagnosis CenterAichiJapan
  3. 3.Graduate School of MedicineGifu UniversityGifuJapan
  4. 4.Nagoya Radiological Diagnosis CenterAichiJapan

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