Diffusion-weighted imaging-based probabilistic segmentation of high- and low-proliferative areas in high-grade gliomas
The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) correlates inversely with tumor proliferation rates. High-grade gliomas are typically heterogeneous and the delineation of areas of high and low proliferation is impeded by partial volume effects and blurred borde...
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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2012 Apr 5
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| In: |
Cancer imaging
Year: 2012, Jahrgang: 12, Heft: 1, Pages: 89-99 |
| ISSN: | 1470-7330 |
| DOI: | 10.1102/1470-7330.2012.0010 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1102/1470-7330.2012.0010 Verlag, Volltext: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3335334/ 
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| Verfasserangaben: | Dirk Simon, Klaus H. Fritzsche, Christian Thieke, Jan Klein, Peter Parzer, Marc-André Weber, Bram Stieltjes |
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| 520 | |a The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) correlates inversely with tumor proliferation rates. High-grade gliomas are typically heterogeneous and the delineation of areas of high and low proliferation is impeded by partial volume effects and blurred borders. Commonly used manual delineation is further impeded by potential overlap with cerebrospinal fluid and necrosis. Here we present an algorithm to reproducibly delineate and probabilistically quantify the ADC in areas of high and low proliferation in heterogeneous gliomas, resulting in a reproducible quantification in regions of tissue inhomogeneity. We used an expectation maximization (EM) clustering algorithm, applied on a Gaussian mixture model, consisting of pure superpositions of Gaussian distributions. Soundness and reproducibility of this approach were evaluated in 10 patients with glioma. High- and low-proliferating areas found using the clustering correspond well with conservative regions of interest drawn using all available imaging data. Systematic placement of model initialization seeds shows good reproducibility of the method. Moreover, we illustrate an automatic initialization approach that completely removes user-induced variability. In conclusion, we present a rapid, reproducible and automatic method to separate and quantify heterogeneous regions in gliomas. | ||
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