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Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme

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

Abstract

Object

Glioblastoma multiforme (GBM) brain tumor is heterogeneous in nature, so its quantification depends on how to accurately segment different parts of the tumor, i.e. viable tumor, edema and necrosis. This procedure becomes more effective when metabolic and functional information, provided by physiological magnetic resonance (MR) imaging modalities, like diffusion-weighted-imaging (DWI) and perfusion-weighted-imaging (PWI), is incorporated with the anatomical magnetic resonance imaging (MRI). In this preliminary tumor quantification work, the idea is to characterize different regions of GBM tumors in an MRI-based semi-automatic multi-parametric approach to achieve more accurate characterization of pathogenic regions.

Materials and methods

For this purpose, three MR sequences, namely T2-weighted imaging (anatomical MR imaging), PWI and DWI of thirteen GBM patients, were acquired. To enhance the delineation of the boundaries of each pathogenic region (peri-tumoral edema, viable tumor and necrosis), the spatial fuzzy C-means algorithm is combined with the region growing method.

Results

The results show that exploiting the multi-parametric approach along with the proposed semi-automatic segmentation method can differentiate various tumorous regions with over 80 % sensitivity, specificity and dice score.

Conclusion

The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

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Authors and Affiliations

  1. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran

    Anahita Fathi Kazerooni, Sahar Rezaei & Hamidreza Saligheh Rad

  2. Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran

    Anahita Fathi Kazerooni, Sahar Rezaei & Hamidreza Saligheh Rad

  3. Neurosurgery Ward, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Meysam Mohseni

  4. Paytakht Imaging Center, Tehran, Iran

    Gholamreza Bakhshandehpour

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  1. Anahita Fathi Kazerooni
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  2. Meysam Mohseni
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Correspondence to Hamidreza Saligheh Rad.

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Fathi Kazerooni, A., Mohseni, M., Rezaei, S. et al. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. Magn Reson Mater Phy 28, 13–22 (2015). https://doi.org/10.1007/s10334-014-0442-7

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  • DOI: https://doi.org/10.1007/s10334-014-0442-7

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