Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia

Can Gafuroğlu, Islem Rekik (Lead / Corresponding author), Alzheimer's Disease Neuroimaging Initiative

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
108 Downloads (Pure)

Abstract

Despite the large body of existing neuroimaging-based studies on brain dementia, in particular mild cognitive impairment (MCI), modeling and predicting the early dynamics of dementia onset and development in healthy brains is somewhat overlooked in the literature. The majority of computer-aided diagnosis tools developed for classifying healthy and demented brains mainly rely on either using single timepoint or longitudinal neuroimaging data. Longitudinal brain imaging data offer a larger time window to better capture subtle brain changes in early MCI development, and its utilization has been shown to improve classification and prediction results. However, typical longitudinal studies are challenged by a limited number of acquisition timepoints and the absence of inter-subject matching between timepoints. To address this limitation, we propose a novel framework that learns how to predict the developmental trajectory of a brain image from a single acquisition timepoint (i.e., baseline), while classifying the predicted trajectory as ‘healthy’ or ‘demented’. To do so, we first rigidly align all training images, then extract ‘landmark patches’ from training images. Next, to predict the patch-wise trajectory evolution from baseline patch, we propose two novel strategies. The first strategy learns in a supervised manner to select a few training atlas patches that best boost the classification accuracy of the target testing patch. The second strategy learns in an unsupervised manner to select the set of most similar training atlas patches to the target testing patch using multi-kernel patch manifold learning. Finally, we train a linear classifier for each predicted patch trajectory. To identify the final label of the target subject, we use majority voting to aggregate the labels assigned by our model to all landmark patches’ trajectories. Our image prediction model boosted the classification performance by 14% point without further leveraging any enhancing methods such as feature selection.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
Place of PublicationSwitzerland
PublisherSpringer
Pages437-445
Number of pages9
Volume11072
ISBN (Electronic)9783030009311
ISBN (Print)9783030009304
DOIs
Publication statusPublished - 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11072
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period16/09/1820/09/18

Fingerprint Dive into the research topics of 'Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia'. Together they form a unique fingerprint.

Cite this