Sidong Liu

Neuroimaging in early detection of Alzheimer's disease with ADNI

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Award
2012 AADRF Top-Up Scholarship
Project Snapshot

Neuroimaging plays an essential role in human clinical neuroscience. As imaging technology advances,  the large-scale high-quality data could provide important insights of the pathology of neurological disorders, such as Alzheimer's disease. In this project, I aim to analyze the neuroimaging data and integrate the findings into computer aided diagnosis pipelines for Alzheimer's disease and mild cognitive impairment (MCI). The main objective of this project is to identify the early signs of cognitive impairment, thus keep contributing to the long-term objective, which is to deliver integrated and patient-centered healthcare to individuals with Alzheimer's disease or MCI.

Detailed Project Summary

The overarching goal of Mr Liu's research is to establish standardized methods for early diagnosis of Alzheimer’s disease with the neuroimaging-based biomarkers. He has made the concrete progress towards achieving my research aims. Firstly, he designed a new framework for multi-stage diagnosis of Alzheimer’s disease (AD). It is well known that mild cognitive impairment (MCI) patients have high risks to develop AD than the normal aging people. Therefore, it is very important to identify the MCI patients among normal aging people, so that we could provide them early medical interventions. This new framework is capable of accurate diagnosis of multi-stage AD, including normal aging, MCI and AD. Secondly, he investigated the imaging data from multiple sources/modalities simultaneously. Research has indicated that multi-modal data could provide complementary information to each other, thus enable him to achieve more accurate diagnosis of AD. Currently, the positron emission tomography (PET) and magnetic resonance imaging (MRI) data were used in his project, but other modalities can also be seamlessly integrated. Thirdly, he designed several intelligent algorithms to detect the disease-specific biomarkers and discover their semantic association to the diseases. In particular, he developed a high-level neural network to mimic the human brain, which could learn the brain atrophy and hypo-metabolism patterns of AD and MCI patients automatically.

Publications and Presentations resulting from award

Journal articles

S. Liu, W. Cai, Y. Song, S. Pujol, R. Kikinis, L. Wen, D. Feng, “Sparse Auto-encoded Hypo-metabolism Patterns in Alzheimer’s Disease and Mild Cognitive Impairment”, The Journal of Nuclear Medicine, vol 54 (Suppl. 2), 1807, 2013. (ERA-Rank-A Journal)

S. Q. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, M. Fulham, D. Feng, "High-level Feature based PET Image Retrieval with Deep Learning Architecture", submitted to The Journal of Nuclear of Medicine. (ERA-Rank-A Journal)

F. Zhang, Y. Song, S. Liu, S. Pujol, R. Kikinis, M. Fulham, D. Feng, W. Cai, "Semantic Association for Neuroimaging Classification of PET Images", submitted to The Journal of Nuclear of Medicine. (ERA-Rank-A Journal)

S. Q. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, "An Adaptive Multi-Modal Neuroimaging Biomarker based Deep Learning Framework for Multi-Stage Diagnosis of Alzheimer’s Disease", submitted to IEEE Transactions on Biomedical Engineering. (ERA-Rank-A Journal)

S. Liu, W. Cai, Y. Song, S. Pujol, R. Kikinis, X. Wang, D. Feng, "Multifold Bayesian Kernelization for Neuroimaging Pattern Classification", submitted to NeuroImage. (ERA-Rank-A Journal)

S. Liu, W. Cai, L. Wen, S. Eberl, M.J. Fulham, D. Feng, "Multi-Channel Neurodegenerative Pattern Analysis and Its Application in Neuroimaging Retrieval For Alzheimer’s Disease and Mild Cognitive Impairment", Computerized Medical Imaging and Graphics (under revision). (ERA-Rank-B Journal)

Conference presentations

S. Liu, Y. Song, W. Cai, S. Pujol, R. Kikinis, X. Wang, D. Feng, "Multifold Bayesian Kernelization in Alzheimer’s Diagnosis", The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013), Part II, LNCS 8150, pp.303-310, 2013. (ERA-Rank-A Conference) (MICCAI Student Travel Award)

S. Liu, W. Cai, Y. Song, S. Pujol, R. Kikinis, D. Feng, "A Bag of Semantic Words Model for Medical Content-based Retrieval", The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) Workshop on Medical Content-based Retrieval for Clinical Decision Support, pp.6-21, 2013. (ERA-Rank-A Conference Workshop) (Oral Presentation)

S. Liu, W. Cai, L. Wen, Y. Song, S. Pujol, R. Kikinis, L. Wen, D. Feng, "Localized Sparse Code Gradient in Alzheimer’s Disease Staging", The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), pp.5398-5401, 2013. (ERA-Rank-A Conference) (Oral Presentation)

S. Liu, L. Zhang, W. Cai, Y. Song, Z. Wang, L. Wen, D. Feng, "A Supervised Multiview Spectral Embedding Method for Neuroimaging Classification", The 20th IEEE International Conference on Image Processing (ICIP 2013), pp. 602-605, 2013. (ERA-Rank-B Conference) (Oral Presentation)

L. Zhang, S. Liu, Z. Wang, W. Cai, Y. Song, D. Feng, "Graph Cuts based Relevance Feedback in Image Retrieval", The 20th IEEE International Conference on Image Processing (ICIP 2013), pp.4358-4362, 2013. (ERA-Rank-B Conference)

S. Liu, W. Cai, L. Wen, D. Feng, "Neuroimaging Biomarker based Prediction of Alzheimer’s Disease Severity with Optimized Graph Construction", IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2013), pp.1324-1327, 2013. (ERA-Rank-A Conference)

S. Liu, W. Cai, L. Wen, D. Feng, "Multi-Channel Brain Atrophy Pattern Analysis in Neuroimaging Retrieval", IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2013), pp.206-209, 2013. (ERA-Rank-A Conference)

S. Q. Liu, S. Liu, W. Cai, S. Pujol, R. Kikinis, D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning”, Accepted by IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2014), 2014. (ERA-Rank-A Conference) (Oral Presentation)

W. Cai, S. Liu, Y. Song, S. Pujol, R. Kikinis, D. Feng, “A 3D Difference of Gaussian based Lesion Detector for Brain PET”, Accepted by IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2014), 2014. (ERA-Rank-A Conference) (Oral Presentation)

H. Che, S. Liu, W. Cai, S. Pujol, R. Kikinis, D. Feng, “Co-neighbor Multi-view Spectral Embedding for Medical Content-based Retrieval”, Accepted by IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2014), 2014. (ERA-Rank-A Conference)

F. Zhang, S. Liu, W. Cai, Y. Song, L. Wen, S. Eberl, M. Fulham, D. Feng, “Automated Feedback Extraction for Medical Imaging Retrieval”, Accepted by IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2014), 2014. (ERA-Rank-A Conference)

Where are they now?

Mr Liu is a PhD candidate based at the School of Information Technologies, University of Sydney. He was awarded a 2012 AADRF PhD top up scholarship has over the course of this year attended the prestigious Harvard Medical School as a research scholar to conduct research on dementia. He will complete his one-year visit soon and return to Sydney in December 2014 to complete his PhD thesis.