Brain MR Image Segmentation: From Multi-Atlas Method To Deep Learning Models

Quantitative analysis of the brain structures on magnetic resonance (MR) images plays a crucial role in examining brain development and abnormality, as well as in aiding the treatment planning. Although manual delineation is commonly considered as the gold standard, it suffers from the shortcomings in terms of low efficiency and inter-rater variability. Therefore, developing automatic anatomical segmentation of human brain is of importance in providing a tool for quantitative analysis (e.g., volume measurement, shape analysis, cortical surface mapping). Despite a large number of existing techniques, the automatic segmentation of brain MR images remains a challenging task due to the complexity of the brain anatomical structures and the great inter- and intra-individual variability among these anatomical structures. To address the existing challenges, four methods are proposed in this thesis. The first work proposes a novel label fusion scheme for the multi-atlas segmentation. A two-stage majority voting scheme is developed to address the over-segmentation problem in the hippocampus segmentation of brain MR images. The second work of the thesis develops a supervoxel graphical model for the whole brain segmentation, in order to relieve the dependencies on complicated pairwise registration for the multi-atlas segmentation methods. Based on the assumption that pixels within a supervoxel are supposed to have the same label, the proposed method converts the voxel labeling problem to a supervoxel labeling problem which is solved by a maximum-a-posteriori (MAP) inference in Markov random field (MRF) defined on supervoxels. The third work incorporates attention mechanism into convolutional neural networks (CNN), aiming at learning the spatial dependencies between the shallow layers and the deep layers in CNN and producing an aggregation of the attended local feature and high-level features to obtain more precise segmentation results. The fourth method takes advantage of the success of CNN in computer vision, combines the strength of the graphical model with CNN, and integrates them into an end-to-end training network. The proposed methods are evaluated on public MR image datasets, such as MICCAI2012, LPBA40, and IBSR. Extensive experiments demonstrate the effectiveness and superior performance of the three proposed methods compared with the other state-of-the-art methods

Canonical Gauge Coupling Unification in the Standard Model with High-Scale Supersymmetry Breaking

Inspired by the string landscape and the unified gauge coupling relation in the F-theory Grand Unified Theories (GUTs) and GUTs with suitable high-dimensional operators, we study the canonical gauge coupling unification and Higgs boson mass in the Standard Model (SM) with high-scale supersymmetry breaking. In the SM with GUT-scale supersymmetry breaking, we achieve the gauge coupling unification at about 5.3 x 10^{13} GeV, and the Higgs boson mass is predicted to range from 130 GeV to 147 GeV. In the SM with supersymmetry breaking scale from 10^4 GeV to 5.3 x 10^{13} GeV, gauge coupling unification can always be realized and the corresponding GUT scale M_U is from 10^{16} GeV to 5.3 x 10^{13} GeV, respectively. Also, we obtain the Higgs boson mass from 114.4 GeV to 147 GeV. Moreover, the discrepancies among the SM gauge couplings at the GUT scale are less than about 4-6%. Furthermore, we present the SU(5) and SO(10) models from the F-theory model building and orbifold constructions, and show that we do not have the dimension-five and dimension-six proton decay problems even if M_U \le 5 x 10^{15} GeV.Comment: RevTex4, 16 pages, 5 figures, version to appear in JHE

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The data set, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes, posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labeling are computer programmed; thus, the data set may be extended easily if a larger scale is desired. The data set is accessible from \url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

Formation mechanism of streamer discharges in liquids: A review

Streamer discharges in liquids have received lots of attention with respect to their considerable applications in various disciplines. Much effort has been spent to understand the basis of streamer ignition and propagation in dielectric liquids, but a comprehensive mechanism is far from conclusive. In this review, based on an introduction to the streamer physics and experimentally observed characteristics in various liquids, the authors emphasise on the current status of streamer discharge ignition mechanisms and present their understanding for each theory