@inbook{9c73dfb1fdb04032b2230901e1137d0c,
title = "Unsupervised Analysis of Alzheimer{\textquoteright}s Disease Signatures Using 3D Deformable Autoencoders",
abstract = "With the increasing incidence of neurodegenerative diseases such as Alzheimer{\textquoteright}s Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer{\textquoteright}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 github.com/ci-ber/MORPHADE.",
keywords = "Classification, Registration, Unsupervised learning",
author = "Avci, {Mehmet Yigit} and Emily Chan and Veronika Zimmer and Daniel Rueckert and Benedikt Wiestler and Schnabel, {Julia A.} and Bercea, {Cosmin I.}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 1st MICCAI Meets Africa Workshop, MImA 2024 and 1st MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024 ; Conference date: 06-10-2024 Through 06-10-2024",
year = "2025",
doi = "10.1007/978-3-031-79103-1_27",
language = "English",
isbn = "9783031791024",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "266--276",
editor = "Udunna Anazodo and Naren Akash and Moritz Fuchs and Celia Cintas and Alessandro Crimi and Tinahse Mutsvangwa and Farouk Dako and Willam Ogallo",
booktitle = "Medical Information Computing - First MICCAI Meets Africa Workshop, MImA 2024, and First MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers",
address = "Germany",
}