10. Statistical Shape Modeling and Analysis Using Imaging Data
The video recording of this talk can be viewed here.
Organizer: Anuj Srivastava
Florida State University
Email: anuj@stat.fsu.edu
Chair: Anuj Srivastava
Florida State University
Email: anuj@stat.fsu.edu
Speakers:
1. Sebastian Kurtek
Ohio State University
Email: kurtek.1@stat.osu.edu
Title: Visualization and Outlier Detection for Shape Data
Time: 2:00pm-2:20pm
Abstract:
We propose a new method for the construction and visualization of geometrically-motivated boxplot displays for elastic curve data. We use a recent shape analysis framework, based on the square-root velocity function representation of curves, to extract different sources of variability from elastic curves, which include location, scale, shape, orientation and parametrization. We then focus on constructing separate displays for these various components using the Riemannian geometry of their representation spaces. This involves computation of a median, two quartiles, and two extremes based on geometric considerations. The outlyingness of an elastic curve is also defined separately based on each of the five components. We evaluate the proposed methods using multiple simulations, and then focus our attention on real data applications. In particular, we study variability in (a) 3D spirals, (b) handwritten signatures, (c) 3D fibers from diffusion tensor magnetic resonance imaging, and (d) trajectories of the Lorenz system. This work was done in collaboration with Weiyi Xie and Oksana Chkrebtii.
2. Zhengwu Zhang
UNC
Email: zhengwu_zhang@unc.edu
Title: Surface-Based Connectivity Integration
Time: 2:20pm-2:40pm
Abstract:
The integration of structural (SC) and functional connectivity (FC) remains a necessary and challenging frontier for neuroscience research due to signal and image processing limitations. Diffusion (dMRI) and resting-state functional MRI (rs-fMRI) provide the signals in white (WM) and gray matter (GM) for SC and FC. The integration of structural and functional connectivity thus far has been limited to atlas-based parcellation studies. We present a novel atlas-free processing pipeline and some analysis methods to explore the integration of structural and functional connectivity at high spatial resolution. This processing pipeline overcomes a few important limitations: 1. it utilizes the geometry of the brain to impose prior knowledge, allowing all white matter fibers to end on the WM-GM surfaces; 2. it smoothes the sparse SC into a dense one for a better comparison with FC. The pipeline also outputs a new biomarker that can be used to study various clinical questions - the integrity/correlation between FC and SC at each vertex on the WM-GM surface. We will demonstrate this pipeline using a few healthy subjects.
3. Chao Huang
Florida State University
Email: chaohuang@stat.fsu.edu
Title: Shape-on-Vector Geodesic Regression Model and Its Applications in Image Data Analysis
Time: 2:40pm-3:00pm
Abstract:
With the rapid growth of modern technology, many large-scale biomedical studies have been conducted to collect massive datasets with large volumes of complex information from increasingly large cohorts. Among these collected images, they usually contain objects of interest (e.g., regions of interest, ROIs) and the major goal is to understand and analyze shapes of these objects in larger biological systems. Due to the complexity of disease progression and mis-registration in image preprocessing, shapes can significantly vary across subjects and distinct shape patterns are more likely to be found within the same patient group. Therefore, understanding such shape heterogeneity is critical for the development of urgently needed approaches to the prevention, diagnosis, and treatment of these diseases, and precision medicine broadly. In order to address this challenge, in this talk, several shape-on-vector regression models are established for heterogeneous imaging data with different structures. This is a joint work with Dr. Anuj Srivastava.