Purdue University Graduate School
Thesis_Shan_Cong_Submitted_20190807.pdf (51.9 MB)

Morphometric Analysis of Hippocampal Subfields

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posted on 2019-10-17, 20:14 authored by Shan CongShan Cong
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disease distinguished by progressive impairment of memory and decline in cognitive abilities. The hippocampus is widely recognized to play essential roles in forming and gradually transferring information from short-term memory into long-term memory, and it is involved in the onset of the neuropathological pathways of the brain to suffer neuron loss in the rise of AD. Thus, hippocampal information obtained from magnetic resonance imaging (MRI) scans have been established as crucial AD biomarkers. The hippocampus is composed of multiple subfields, and the neuron loss is not uniformly distributed on the whole hippocampus. However, this critical subfield information is not addressed by the existing surface-based morphometry (SBM) and voxel-based morphometry (VBM) studies. Due to the size, complexity, heterogeneity, and folding anatomy of the hippocampus, acquiring volumetric and morphometric measures of hippocampal subfields usually presents not only technical challenges in quantitative neuroimaging but also analytical challenges. To address these challenges and deeply understand the relationships between hippocampal shape changes and brain disorders, especially to examine the degeneration of hippocampal subfields, this thesis focuses on constructing a hippocampal subfield morphometric analysis framework including the following aspects: 1) hippocampal subfield segmentation; 2) 3D shape modeling; 3) feature formulation; 4) diffeomorphic surface registration; 5) surface shape reconstruction; and 6) association analytics. The goals include developing accurate hippocampal subfield guided registration methods, extracting useful features and identifying significant subfields on the hippocampus that are highly related to cognitive disabilities, and using such information to assist early detection of AD.


R01 EB022574 and R01 LM011360 to LS, P30 AG10133, R01 AG19771 and U01 AG024904 (IU Subcontract) to AJS, R01 AG040770 to LA, and K01 AG049050 to SLR


Degree Type

  • Doctor of Philosophy


  • Electrical and Computer Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Maher Rizkalla

Advisor/Supervisor/Committee co-chair

Edward J. Delp

Additional Committee Member 2

Li Shen

Additional Committee Member 3

Paul Salama

Additional Committee Member 4

Zhongming Liu