@inproceedings{895dd71f012c4eebb1d973076cd2d378,
title = "Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI",
abstract = "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.",
keywords = "Cell density, Lattice-Boltzmann method, Longitudinal MRI, Reaction-diffusion equation, Tumor growth model, Tumor segmentation",
author = "Linmin Pei and Reza, {Syed M.S.} and Wei Li and Christos Davatzikos and Iftekharuddin, {Khan M.}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Computer-Aided Diagnosis ; Conference date: 13-02-2017 Through 16-02-2017",
year = "2017",
doi = "10.1117/12.2254034",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Petrick, {Nicholas A.} and Armato, {Samuel G.}",
booktitle = "Medical Imaging 2017",
}