\documentclass{midl} 

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\jmlrvolume{-- Under Review}
\jmlryear{2020}
\jmlrworkshop{Full Paper -- MIDL 2020}
\editors{Under Review for MIDL 2020}

\title[Uncertiny-based GCN for organ segmentation refinement]{Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement}

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%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Roger D. Soberanis-Mukul\nametag{$^{1}$}} \Email{roger.soberanis@tum.de}\\
\Name{Nassir Navab\nametag{$^{1,2}$}} \Email{nassir.navab@tum.de}\\
\Name{Shadi Albarqouni\nametag{$^{1,3}$}} \Email{salbarqouni@ethz.ch}\\
\addr $^{1}$ Computer Aided Medical Procedures, Technische Universit\"{a}t M\"{u}nchen, Germany \\
\addr $^{2}$ Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA \\
\addr $^{3}$ Computer Vision Laboratory, ETH Zurich, Switzerland
}

\begin{document}

\maketitle

\begin{abstract}
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks have dominated the state of the art in this task. However, since this problem presents a challenging environment due to high variability in the organ's shape and similarity between tissues, the generation of false negative and false positive regions in the output segmentation is a common issue. Recent works have shown that the uncertainty analysis of the model can provide us with useful information about potential errors in the segmentation. In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem that is solved by training a graph convolutional network. To test our method we refine the initial output of a 2D U-Net. We validate our framework with the NIH pancreas dataset and the spleen dataset of the medical segmentation decathlon. We show that our method outperfroms the state-of-the art CRF refinement method by improving the dice score by 1\% for the pancreas and 2\% for spleen, with respect to the original U-Net's prediction. Finally, we discuss the results and current limitations of the model for future work in this research direction. For reproducibility purposes, we make our code publicly available\footnote[1]{https://github.com/rodsom22/gcn\_refinement}.
\end{abstract}

\begin{keywords}
Organ segmentation refinement, Uncertainty Quantification, Graph Convolutional Networks, Semi-Supervised Learning
\end{keywords}


\input{introduction.tex}
\input{method.tex}
\input{exp_res.tex}
\input{conclusion.tex}


\midlacknowledgments{
R. D. S. is supported by Consejo Nacional de Ciencia y Tecnolog\'{i}a (CONACYT), Mexico. 
S.A. is supported by the PRIME programme of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF).
}


\bibliography{soberanis20}

\end{document}
