# Also in the Article

Error measure
This protocol is extracted from research article:
Estimating the extent of glioblastoma invasion
J Math Biol, Jan 26, 2021;

Procedure

In Sect. 4.3 we will investigate the impact of the stationalization error on the observed tumor front. In this course we will compare the reconstructed tumor front of a fully instationary simulation with the reconstructed tumor front using the stationalization approach.

Given a reference density distribution $ua(x,t):Ω×T↦R$, and a stationary approximation $ub(x):Ω↦R$ and a threshold value $θ∈R$ we define two domains A and B as

The medically relevant information is the spatial discrepancy between two level-set surfaces ($∂A,∂B$) of these density profiles. An absolute measure for this error is the symmetric difference $|A⊕B|$, as depicted in Fig. 3. It describes those volumes, which are either included A but not in B, or vice versa. That way, both over- and underestimations of the approximation $ub$ are represented. The symmetric difference is however not comparable between 1D, 2D and 3D simulations.

left: Symmetric difference region between the two level-sets $|A⊕B|$: gray regions. right: localized sketch of the symmetric difference region, the surface of the level-set volume $|∂A|$ and the distance between the two level-sets $LB$

The most expressive information in the medical context is the average distance between the two level-sets. We therefore introduce the global characteristic level-set distance.

(Global characteristic level-set distance) For a given level-set value $θ$, we define the characteristic level-set distance between $∂A$ and $∂B$ as

It quantifies the average distance between the two level-sets. Assuming a spherical reference geometry for A we can approximate this expression in the following way. Given the radius $rA$, $LB$ simplifies to

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