TY - GEN
T1 - Regional abnormality representation learning in structural MRI for AD/MCI diagnosis
AU - Choi, Jun Sik
AU - Lee, Eunho
AU - Suk, Heung Il
N1 - Funding Information:
Acknowledgement. This work was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A01052216); and also by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2016941946).
Funding Information:
This work was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A01052216); and also by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2016941946).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - In this paper, we propose a novel method for MRI-based AD/MCI diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches in a unified framework. Specifically, we parcellate a brain into predefined regions by using anatomical knowledge, i.e., template, and find complex nonlinear relations among voxels, whose intensity denotes the volumetric measure in our case, within each region. Unlike the existing methods that mostly use a cubical or rectangular shape, we regard the anatomical shape of regions as atypical forms of patches. Using the complex nonlinear relations among voxels in each region learned by deep neural networks, we extract a regional abnormality representation. We then make a final clinical decision by integrating the regional abnormality representations over a whole brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing them in a brain space individually. We validated the efficacy of our method in experiments with baseline MRI dataset in the ADNI cohort by achieving promising performances in three binary classification tasks.
AB - In this paper, we propose a novel method for MRI-based AD/MCI diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches in a unified framework. Specifically, we parcellate a brain into predefined regions by using anatomical knowledge, i.e., template, and find complex nonlinear relations among voxels, whose intensity denotes the volumetric measure in our case, within each region. Unlike the existing methods that mostly use a cubical or rectangular shape, we regard the anatomical shape of regions as atypical forms of patches. Using the complex nonlinear relations among voxels in each region learned by deep neural networks, we extract a regional abnormality representation. We then make a final clinical decision by integrating the regional abnormality representations over a whole brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing them in a brain space individually. We validated the efficacy of our method in experiments with baseline MRI dataset in the ADNI cohort by achieving promising performances in three binary classification tasks.
UR - http://www.scopus.com/inward/record.url?scp=85054479364&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00919-9_8
DO - 10.1007/978-3-030-00919-9_8
M3 - Conference contribution
AN - SCOPUS:85054479364
SN - 9783030009182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 72
BT - Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Liu, Mingxia
A2 - Suk, Heung-Il
A2 - Shi, Yinghuan
PB - Springer Verlag
T2 - 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -