which is defined as the number of pixels in the final segmentation $\vec{y}^K$ that are labelled differently compared to the corresponding pixels in the original phantom $\vec{y}_{\text{init}}$. Only the pixels in the inner disk of the phantoms are taken into account. All experiments are repeated for and averaged over $100$ runs with different phantoms.

The creation of random phantoms is implemented in [<span style="font-variant:small-caps;">Matlab</span>]{}. The remainder of the experiment setup scripts are implemented in Python. The reconstruction algorithms, including the MC-DART algorithm, are implemented in Python, where the ASTRA Toolbox [@ASTRA2; @ASTRA] is used to take care of the ARM invocations and forward projections, including the masking in each MC-DART iteration and the creation of matrices $\vec{W}$ and $\overline{\vec{W}}^k$ based on the geometric properties.

Figure \[fig:StandardResults\] shows the percentage of misclassified pixels with respect to the number of pixels in the inner disk. The percentage is lowest when the number of materials is low and the number of channels is high, while the percentage is highest when the number of channels is low and the number of materials is high. Given a number of channels, the percentage seems to scale logarithmically with the number of materials. On the other hand, 