Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd

Uncertainty Quantification, Image Synthesis and Deformation Prediction for Image Registration

Image registration is essential for medical image analysis to provide spatial correspondences. It is a difficult problem due to the modeling complexity of image appearance and the computational complexity of the deformable registration models. Thus, several techniques are needed: Uncertainty measurements of the high-dimensional parameter space of the registration methods for the evaluation of the registration result; Registration methods for registering healthy medical images to pathological images with large appearance changes; Fast registration prediction techniques for uni-modal and multi-modal images. This dissertation addresses these problems and makes the following contributions: 1) A frame- work for uncertainty quantification of image registration results is proposed. The proposed method for uncertainty quantification utilizes a low-rank Hessian approximation to evaluate the variance/co- variance of the variational Gaussian distribution of the registration parameters. The method requires significantly less storage and computation time than computing the Hessian via finite difference while achieving excellent approximation accuracy, facilitating the computation of the variational approximation; 2) An image synthesis deep network for pathological image registration is developed. The network transforms a pathological image into a ‘quasi-normal’ image, making registrations more accurate; 3) A patch-based deep learning framework for registration parameter prediction using image appearances only is created. The network is capable of accurately predicting the initial momentum for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model for both uni-modal and multi-modal registration problems, while increasing the registration speed by at least an order of magnitude compared with optimization-based approaches and maintaining the theoretical properties of LDDMM. Applications of the methods include 1) Uncertainty quantification of LDDMM for 2D and 3D medical image registrations, which could be used for uncertainty-based image smoothing and subsequent analysis; 2) Quasi-normal image synthesis for the registration of brain images with tumors with potential extensions to other image registration problems with pathologies and 3) deformation prediction for various brain datasets and T1w/T2w magnetic resonance images (MRI), which could be incorporated into other medical image analysis tasks such as fast multi-atlas image segmentation, fast geodesic image regression, fast atlas construction and fast user-interactive registration refinement.Doctor of Philosoph