Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks

Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior

We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. First, we utilize the ground-truth brain tumor lesions from a group of patients to generate the heatmaps of different types of lesions. These heatmaps are used to create the volume-of-interest (VOI) map which contains prior information about brain tumor lesions. The VOI map is then integrated with the multimodal MR images and input to a 3D U-Net for segmentation. The proposed method is evaluated on a public benchmark dataset, and the experimental results show that the proposed feature fusion method achieves an improvement over the baseline methods. In addition, our proposed method also achieves a competitive performance compared to state-of-the-art methods.Comment: 5 pages, 4 figures, 1 table, LNCS forma

A Critical Review onMathematical Descriptions to Study Flux Processes and Environmental-Related Interactions ofMangroves

Trees are resources that provide multiple benefits, such as the conservation of fauna, both terrestrial and marine, a source of food and raw material, and offering protection in storms, which makes it practical to understand their behavior against different phenomena. Such understanding may be possible through process modeling. Studies confirm that mangrove forests can store more carbon than other forests, influencing the fight against global warming. Thus, a critical and systematic review was carried out regarding studies focusing on mangroves to collect information on the models that have been applied and the most influential variables highlighted by other authors. Applying a systematic search for the most relevant topics related to mangroves (basic as well as recent information), it is possible to group models and methods carried out by other authors to respond to certain behaviors presented by mangroves. Moreover, possible structuring of a mathematical model applied to a species of interest thanks to the analyzed references could provide justified information to the authorities on the importance of these forests and the benefits of their preservation and regeneration-recovery.Trees are resources that provide multiple benefits, such as the conservation of fauna, both terrestrial and marine, a source of food and raw material, and offering protection in storms, which makes it practical to understand their behavior against different phenomena. Such understanding may be possible through process modeling. Studies confirm that mangrove forests can store more carbon than other forests, influencing the fight against global warming. Thus, a critical and systematic review was carried out regarding studies focusing on mangroves to collect information on the models that have been applied and the most influential variables highlighted by other authors. Applying a systematic search for the most relevant topics related to mangroves (basic as well as recent information), it is possible to group models and methods carried out by other authors to respond to certain behaviors presented by mangroves. Moreover, possible structuring of a mathematical model applied to a species of interest thanks to the analyzed references could provide justified information to the authorities on the importance of these forests and the benefits of their preservation and regeneration-recovery

The complexity of asynchronous model based testing

This is the post-print version of the final paper published in Theoretical Computer Science. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.In model based testing (MBT), testing is based on a model MM that typically is expressed using a state-based language such as an input output transition system (IOTS). Most approaches to MBT assume that communications between the system under test (SUT) and its environment are synchronous. However, many systems interact with their environment through asynchronous channels and the presence of such channels changes the nature of testing. In this paper we investigate the situation in which the SUT interacts with its environment through asynchronous channels and the problems of producing test cases to reach a state, execute a transition, or to distinguish two states. In addition, we investigate the Oracle Problem. All four problems are explored for both FIFO and non-FIFO channels. It is known that the Oracle Problem can be solved in polynomial time for FIFO channels but we also show that the three test case generation problems can also be solved in polynomial time in the case where the IOTS is observable but the general test generation problems are EXPTIME-hard. For non-FIFO channels we prove that all of the test case generation problems are EXPTIME-hard and the Oracle Problem in NP-hard, even if we restrict attention to deterministic IOTSs

Determination of two-photon exchange amplitudes from elastic electron-proton scattering data

Using the available cross section and polarization data for elastic electron-proton scattering, we provide an extraction of the two-photon exchange amplitudes at a common value of four-momentum transfer, around Q^2 = 2.5 GeV^2. This analysis also predicts the e^+ p / e^- p elastic scattering cross section ratio, which will be measured by forthcoming experiments.Comment: 4 pages, 5 figures, updated error analysi

Probabilistic Analysis and Design of a Raked Wing Tip for a Commercial Transport

An approach for conducting reliability-based design and optimization (RBDO) of a Boeing 767 raked wing tip (RWT) is presented. The goal is to evaluate the benefits of RBDO for design of an aircraft substructure. A finite-element (FE) model that includes eight critical static load cases is used to evaluate the response of the wing tip. Thirteen design variables that describe the thickness of the composite skins and stiffeners are selected to minimize the weight of the wing tip. A strain-based margin of safety is used to evaluate the performance of the structure. The randomness in the load scale factor and in the strain limits is considered. Of the 13 variables, the wing-tip design was controlled primarily by the thickness of the thickest plies in the upper skins. The report includes an analysis of the optimization results and recommendations for future reliability-based studies

Solving the discretised multiphase flow equations with interface capturing on structured grids using machine learning libraries

The authors would like to acknowledge the following EPSRC grants: the PREMIERE programme grant, “AI to enhance manufacturing, energy, and healthcare” (EP/T000414/1); ECO-AI, “Enabling CO capture and storage using AI” (EP/Y005732/1); MUFFINS, “MUltiphase Flow-induced Fluid-flexible structure InteractioN in Subsea” (EP/P033180/1); WavE-Suite, “New Generation Modelling Suite for the Survivability of Wave Energy Convertors in Marine Environments” (EP/V040235/1); INHALE, “Health assessment across biological length scales” (EP/T003189/1); AI-Respire, “AI for personalised respiratory health and pollution” (EP/Y018680/1); RELIANT, “Risk EvaLuatIon fAst iNtelligent Tool for COVID19” (EP/V036777/1); and CO-TRACE, “COvid-19 Transmission Risk Assessment Case Studies — education Establishments” (EP/W001411/1). Also, the authors acknowledge the Innovate UK grant D-XPERT, “AI-Powered Total Building Management System“ (TS/Y020324/1). Support from Imperial-X’s Eric and Wendy Schmidt Centre for AI in Science (a Schmidt Futures program) is gratefully acknowledged. The authors state that, for the purpose of open access, a Creative Commons Attribution (CC BY) license will be applied to any Author Accepted Manuscript version relating to this article.Peer reviewe

Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

[This corrects the article DOI: 10.3389/fnins.2019.01449.]