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Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Object

Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only.

Materials and methods

Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2–7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes.

Results

The highest similarity index between manual and automatic segmentation (SImanual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R 2 = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R 2 = 0.96).

Conclusion

Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.

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Acknowledgments

This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine (www.ctmm.nl), project VOLTA (grant 05T-201).

Conflict of interest

The authors have no conflict of interest.

Ethical standards

All animal experiments were performed according to the Directive 2010/63/EU of the European Parliament and approved by the Animal Care and Use Committee of Maastricht University.

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Authors and Affiliations

  1. Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

    Stefanie J. C. G. Hectors, Igor Jacobs, Gustav J. Strijkers & Klaas Nicolay

  2. Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Gustav J. Strijkers

Authors
  1. Stefanie J. C. G. Hectors
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  2. Igor Jacobs
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  3. Gustav J. Strijkers
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  4. Klaas Nicolay
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Corresponding author

Correspondence to Stefanie J. C. G. Hectors.

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Hectors, S.J.C.G., Jacobs, I., Strijkers, G.J. et al. Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast. Magn Reson Mater Phy 28, 363–375 (2015). https://doi.org/10.1007/s10334-014-0472-1

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  • DOI: https://doi.org/10.1007/s10334-014-0472-1

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