@inproceedings{27bb14a8fafb4b6e8de982fdf798a5de,
title = "Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia",
abstract = "Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers{\textquoteright} Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.",
author = "Vikram Venkatraghavan and Florian Dubost and Bron, {Esther E.} and Niessen, {Wiro J.} and {de Bruijne}, Marleen and Stefan Klein",
year = "2019",
doi = "10.1007/978-3-030-20351-1_13",
language = "English",
isbn = "978-303020350-4",
volume = "11492",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "169--180",
editor = "Chung, {Albert C.S.} and Gee, {James C.} and Yushkevich, {Paul A.} and Siqi Bao",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",
note = "26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
}