to find the exact shape as well as the position of the tumor inside the liver. Interventional radiology is utilized to generate a volumetric cone-beam C-arm computed tomography () [@strobel20093d] image of the patient’s abdomen, which is processed to precisely outline and label the lesion. The toxicity of decreases, the less healthy tissue is labeled as pathologic. The efficacy of the therapy increases, the less cancerous tissue is falsely labeled as healthy [@lo2002randomized]. However, precisely outlining the tumor is challenging, especially due to its variations in size and shape, as well as a high diversity in X-ray attenuation coefficient values representing the lesion as illustrated in **Fig.**\[fig:hepatic\_tumor\_segmentation\_outcome\]. While fully automated systems may yield insufficiently accurate segmentation results, tend to be well suited for an application during .

Methods {#sec:methods}
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Segmentation Method {#sec:segmentation_method}
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 [@vezhnevets2005growcut] is a seeded image segmentation algorithm based on cellular automaton theory. The automaton is a tuple , where $\mathbf{G}_\mathbf{I}$ is the $\mathbf{I}$, where the pixels/voxels act as nodes $\mathbf{v}_e$. The nodes are connected by edges on a grid defined by the Moore neighborhood system. $$\mbox{$\mathbf{Q}\ni\mathbf{Q}_e^t=\left((\mathbf{p}_e, \,\mathbf{\ell}_e^t), \,\mathbf{\Theta}_e^t, \,\mathbf{c}_e, \,\mathbf{h}_e^t\right)$}
\label{eq:growcutgraph}$$ of node $e$ at iteration $t$. the node’s characteristics. Here, we additionally define $\mathbf{h}_e^t \in 