\section{Discussion and Conclusion}

In this work, we have presented a method to construct a sparse semi-labeled graph representation of volumetric medical data, based on the output and uncertainty analysis of a CNN model. We have also shown that graph semi-supervised learning can be used to obtain a refined segmentation. Future research can be directed in the following directions: 

\paragraph{2D vs. 3D Models:} In our experiments, we employed a 2D architecture since it provides us with all necessary components to test out method. Nevertheless, our refinement strategy is orthogonal to other segmentation approaches and can be applied to any CNN that produces uncertainty measures with MCDO. This also includes 3D models. However, to our best knowledge, MCDO is most commonly employed with 2D models and its translation to 3D might require additional methodological efforts derived from working with 3D architectures together with additional requirements for data-handling due to memory constraints \cite{bib:LaBonte19}. Further, 3D model might not necessarily provide a better initial segmentation compared to a 2D CNN as reported by \cite{bib:yuyin19,bib:Wang19}. Nevertheless, investigating our approach using 3D models equipped with MCDO might be an interesting direction. 

\paragraph{Uncertainty Quantification:} In this work, we have employed MCDO \cite{bib:Kendall17} for the model uncertainty analysis, and found out the expectation could be a good choice for well-trained models, while our GCN refinement show superior performance, compared to the expectation, in low-data regime. Nonetheless, recent proposed uncertainty measures \cite{tomczack2019learn}, which disentangle the models uncertainty from the one associated with the inter/intra-observer variability, might be desirable. 

\paragraph{Graph Representation:} We have investigated different connectivity and weighting mechanisms in defining our graph, and extracted a couple of features to represent our nodes. However, prior knowledge, e.g. geometry, could be used to constrain the ROI and provide plausible configurations \cite{degel2018domain,oktay2017anatomically}.