TY - JOUR
T1 - Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
AU - Lian, Chunfeng
AU - Liu, Mingxia
AU - Zhang, Jun
AU - Shen, Dinggang
N1 - Funding Information:
This study was supported by NIH grants (EB008374, AG041721, AG042599, EB022880). Data used in this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The investigators within the ADNI did not participate in analysis or writing of this study. A complete list of ADNI investigators can be found online. C. Lain and M. Liu are co-first authors of this paper.
Funding Information:
This study was supported by NIH grants (EB008374, AG041721, AG042599, EB022880). Data used in this paper were obtained from the Alzheimer s Disease Neuroimaging Initiative (ADNI) dataset. The investigators within the ADNI did not participate in analysis or writing of this study. A complete list of ADNI investigators can be found online. C. Lain and M. Liu are co-first authors of this paper.
Publisher Copyright:
© 1979-2012 IEEE.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
AB - Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
KW - Computer-aided Alzheimer's disease diagnosis
KW - discriminative atrophy localization
KW - fully convolutional networks
KW - structural MRI
KW - weakly-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85058985521&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2018.2889096
DO - 10.1109/TPAMI.2018.2889096
M3 - Article
C2 - 30582529
AN - SCOPUS:85058985521
VL - 42
SP - 880
EP - 893
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
IS - 4
M1 - 8585141
ER -