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. In order 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: Lattice-Boltzmann method; Tumor segmentation; cell density; longitudinal MRI; reaction-diffusion equation; tumor growth model.

Figure 1

Pipeline of the proposed method. At the 1^st scan date, we use the label map of 1^st scan image as input of tumor growth model, so that we can predict the cell density distribution of 2^nd scan data. Adding the cell density with other features, such as fractal, mBm and intensity, etc., applying to the RF classifier for predict the label of 2^nd scan data.

Figure 2

Illustration of a lattice with D2Q9 model.

Figure 3

Algorithm for tumor growth model by using LMB.

Figure 4

An example of tumor growth modeling. Tumor seed locates at position (90, 73). We set D = 0.05, ρ = 0.01 and threshold = 0.8. Location of cell density over the threshold will be assigned as tumor. (a) is showing tumor with 200 days, (b) is simulation of tumor with 250 days and (c) is for 300 days.

Figure 5

An example of tumor growth model by using proposed method for one slice of patient 439. (a) Simulated necrosis with D = 0.052, ρ = 0.01. (b) Edema with D = 0.06, ρ = 0.009. (c) Non-enhancing tumor with D = 0.03, ρ = 0.014. (d) Enhancing tumor with D = 0.05, ρ = 0.01. (e) Fused label and (f) ground truth of the second scan data.

Figure 6

Examples of tumor segmentation by using the proposed method. (a) Tumor segmentation without cell density feature, (b) tumor segmentation with cell density feature, and (c) the ground truth of second time scan.

Figure 7

Comparison of DSC between with and without cell density feature by using Leave-One-Out (left column) and 3-Folder cross validation (right column) for all slices, respectively.