3 March 2017 Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI
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In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Linmin Pei, Linmin Pei, Syed M. S. Reza, Syed M. S. Reza, Wei Li, Wei Li, Christos Davatzikos, Christos Davatzikos, Khan M. Iftekharuddin, Khan M. Iftekharuddin, } "Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342L (3 March 2017); doi: 10.1117/12.2254034; https://doi.org/10.1117/12.2254034

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