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Magnetic Resonance Materials in Physics, Biology and Medicine

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01.02.2015 | Research Article

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

verfasst von: Anahita Fathi Kazerooni, Meysam Mohseni, Sahar Rezaei, Gholamreza Bakhshandehpour, Hamidreza Saligheh Rad

Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine | Ausgabe 1/2015

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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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http://martinos.org/qtim/miccai2013/

Titel: Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme
verfasst von: Anahita Fathi Kazerooni
Meysam Mohseni
Sahar Rezaei
Gholamreza Bakhshandehpour
Hamidreza Saligheh Rad
Publikationsdatum: 01.02.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine / Ausgabe 1/2015
Print ISSN: 0968-5243
Elektronische ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-014-0442-7

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