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\begin{document}
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\title{Unsupervised Analysis of  Alzheimer’s Disease Signatures using 3D Deformable Autoencoders}
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\titlerunning{MORPHADE}
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 \author{Mehmet Yigit Avci$^*$\inst{1} 
 \and
 Emily Chan$^*$\inst{1,2,4} \and 
 Veronika Zimmer\inst{1} 
 \and
 Daniel Rueckert\inst{1,3,5} \and
  Benedikt Wiestler\inst{1,3} 
 \and
 Julia A. Schnabel\inst{1,2,4} \and 
 Cosmin I. Bercea\inst{1,2}}
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 \authorrunning{M.Y. Avci et al.}
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 \institute{Technical University of Munich, Germany \and
 Helmholtz Center Munich, Germany \and
 Klinikum Rechts der Isar, Munich, Germany \and  King’s College London, United Kingdom \and Imperial College London, United Kingdom\\ \email{\{cosmin.bercea@tum.de\}}}
%\author{Anonymous}
%\authorrunning{Anonymous et al.}
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\def\thefootnote{*}\footnotetext{These authors contributed equally.}\def\thefootnote{\arabic{footnote}}
% \def\thefootnote{**}\footnotetext{Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: \url{http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf}}\def\thefootnote{\arabic{footnote}}
% STATEMENT OF CONTRIBUTION
% We innovatively use deformation fields and negative Jacobian-which are normally undesired- for anomaly detection with dual-head deformation autoencoders. We demonstrate the method's efficiency on a complex task, Alzheimer's Disease.
\begin{abstract}

With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present \mbox{\emph{MORPHADE}} (Morphological Autoencoders for Alzheimer's Disease Detection), a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. To the best of our knowledge, this is the first use of deformations with deep unsupervised learning to not only detect, but also localize and assess the severity of structural changes in the brain due to AD. We obtain markedly higher anomaly scores in clinically important areas of the brain in subjects with AD compared to healthy controls, showcasing that our method is able to effectively locate AD-related atrophy. We additionally observe a visual correlation between the severity of atrophy highlighted in our anomaly maps and medial temporal lobe atrophy scores evaluated by a clinical expert. Finally, our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines. We believe our framework shows promise as a tool towards improved understanding, monitoring and detection of AD. To support further research and application, we have made our code publicly available at \url{github.com/ci-ber/MORPHADE}.

\keywords{Unsupervised learning \and Registration \and Classification}
\end{abstract}

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