Noel M. Naughton^{1}, Nicolas R. Gallo^{2}, Aaron T. Anderson^{3,4}, and John J. Georgiadis^{1,2}

Results are presented for a random forest model to estimate skeletal muscle microstructure parameters from

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Figure 1. Comparison of random forest model’s microstructural parameter prediction for noise-free simulations and with added SNR of 40. The random forest model is less sensitive to parameter changes related to extracellular organization such as volume fraction or extracellular diffusion. Diameter is underestimated for larger myocyte sizes and permeability is estimated with an offset. When noise is added to the data all parameters fits are affected with packing fraction and extracellular diffuion the most affected.

Figure 2: Histogram of random forest model error compared with testing data set generated from a Sobol sequence for A) noise-free data and B) added SNR of 40. The random forest model’s fit is worse when noise is added. Overall accuracy of the RF model for each parameter is similar to the results for Figure 1 with diameter and intracellular diffusion most accurate and volume fraction, permeability and extracellular diffuion the least accurate.

Figure 3.
Histogram of results for random forest model’s microstructural estimates for
porcine myocardium for four ROI’s (4x4x4 voxels) manually selected from the
center of 2 different porcine hearts. Ranges of the fitted parameters are
substantially smaller than the parameter ranges used for the training data and
are of values expected for ex-vivo
porcine myocardium (volume fraction ~ 0.80, larger permeability from membrane
degradation).

Table 1: Parameter ranges of the Sobol sequence used
to generate training and testing data sets. Ranges were extended to include
physiologically typical parameter values as well as values beyond these ranges
in order not to constrain the random forest model.

Table 2: R^{2} coefficients of random forest model for increasing number of trees showing a diminished increase in the predictive power of the random forest model for more than 100 trees. R^{2} coefficients also show that the random forest model is most accurate for cell diameter and intracellular diffusion, a result also illustrated in Figure 3.