NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Severe early onset preeclampsia: short and long term clinical, psychosocial and biochemical aspects

Preeclampsia is a pregnancy specific disorder commonly defined as de novo hypertension and proteinuria after 20 weeks gestational age. It occurs in approximately 3-5% of pregnancies and it is still a major cause of both foetal and maternal morbidity and mortality worldwide1. As extensive research has not yet elucidated the aetiology of preeclampsia, there are no rational preventive or therapeutic interventions available. The only rational treatment is delivery, which benefits the mother but is not in the interest of the foetus, if remote from term. Early onset preeclampsia (<32 weeks’ gestational age) occurs in less than 1% of pregnancies. It is, however often associated with maternal morbidity as the risk of progression to severe maternal disease is inversely related with gestational age at onset2. Resulting prematurity is therefore the main cause of neonatal mortality and morbidity in patients with severe preeclampsia3. Although the discussion is ongoing, perinatal survival is suggested to be increased in patients with preterm preeclampsia by expectant, non-interventional management. This temporising treatment option to lengthen pregnancy includes the use of antihypertensive medication to control hypertension, magnesium sulphate to prevent eclampsia and corticosteroids to enhance foetal lung maturity4. With optimal maternal haemodynamic status and reassuring foetal condition this results on average in an extension of 2 weeks. Prolongation of these pregnancies is a great challenge for clinicians to balance between potential maternal risks on one the eve hand and possible foetal benefits on the other. Clinical controversies regarding prolongation of preterm preeclamptic pregnancies still exist – also taking into account that preeclampsia is the leading cause of maternal mortality in the Netherlands5 - a debate which is even more pronounced in very preterm pregnancies with questionable foetal viability6-9. Do maternal risks of prolongation of these very early pregnancies outweigh the chances of neonatal survival? Counselling of women with very early onset preeclampsia not only comprises of knowledge of the outcome of those particular pregnancies, but also knowledge of outcomes of future pregnancies of these women is of major clinical importance. This thesis opens with a review of the literature on identifiable risk factors of preeclampsia

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Constraining the supersymmetric parameter space with early data from the Compact Muon Solenoid experiment

The year 2010 saw the Large Hadron Collider (LHC) collect 35:1 pb-1 of 7TeV proton-proton collision data. This thesis reports on the work carried out by the candidate as part of the calculation of the first constraints placed upon the supersymmetric parameter space using measurements made with this data. In particular, the development and application of the kinematic techniques used to ensure that the search was robust to detector mismeasurements, inherent in any early phase of data-taking, are discussed. The Constrained Minimally Supersymmetric Standard Model (CMSSM) is introduced to demonstrate how supersymmetry may extend the Standard Model of particle physics, and is used as the benchmark signal to investigate how supersymmetry may appear in 7TeV proton-proton collisions. The role of kinematics in early searches for such signals is then discussed; given the final state topology of interest (particle jets and large missing transverse momentum), particular attention is paid to errors that are due to detector mismeasurements, and how these may be accounted for with an appropriate choice of observable. A search strategy based upon these principles and applied to the Compact Muon Solenoid (CMS) experiment is then described, as used in the first published search for supersymmetry with LHC data reported in Phys. Lett. B 698 (2011) 196. The kinematic characterisation of events discussed above is exploited to ensure that the search is robust to mismeasurement. The thesis concludes with a summary of the search results. The observed number of events fulfilling the signal criteria is compatible with that expected from the Standard Model alone. The subsequent exclusion limits, given at the 95% Confidence Level, place significantly greater constraints upon the supersymmetric parameter space than those of previous experiments

CERNatschool/SimLUCID-lite: Initial official version (v2.0) - updated for IRIS.

SimLUCID-lite is a simple GEANT4-based simulation of the Langton Ultimate Cosmic ray Intensity Detector (LUCID) experiment, used to estimate the expected data rates observed in Low Earth Orbit (LEO). This is the official initial release of the SimLUCID-lite code that accompanies the CHEP 2013 paper, with updates to link CERN@school with the Institute for Research in Schools

gridpp/gridpp-userguide-doc: Initial version (v1.0).

The GridPP Collaboration is a community of particle physicists and computer scientists based in the United Kingdom and at CERN. It supports tens of thousands of CPU cores and petabytes of data storage across the UK which, amongst other things, played a crucial role in the discovery of the Higgs boson at CERN's Large Hadron Collider. The aim of the document - an offline version of the online GridPP UserGuide - that is built by this code is to help new users join this community and access these resources to make a difference to the world beyond the realm of particle physics

The CERN@school Programme: MoEDAL Grid User Guide

This document provides a guide for those associated with the MoEDAL Collaboration who wish to use the Worldwide LHC Computing Grid (WLCG) resources for collaboration activities, including (but not limited to) running simulations, storing experimental and simulated data, and analsying the results for publication. The guide is largely based on a similar guide written for the UK's GridPP Collaboration that covers a suite of software tools and infrastructure developed or adopted for the benefit of user communities new to the Grid, namely GridPP DIRAC, Ganga, and CernVM. It focusses largely on running GEANT4 simulations of the MoEDAL experiment, with the MoEDAL software deployed with the CernVM File System (CernVM-FS or CVMFS), job and data management with the GridPP DIRAC instance, and Ganga (running on a CernVM) as the Grid User interface

CERNatschool/getting-started: Initial official version (v2.0) - updated for IRIS.

This repository contains legacy code associated with training material for the CERN@school programme; see (for example) CAS-PUB-GEN-000012. This initial release has been updated to reflect CERN@school's move to the Institute for Research in Schools

gridpp/user-guides: Initial version (v1.0).

The GridPP Collaboration is a community of particle physicists and computer scientists based in the United Kingdom and at CERN. It supports tens of thousands of CPU cores and petabytes of data storage across the UK which, amongst other things, played a crucial role in the discovery of the Higgs boson at CERN's Large Hadron Collider. The aim of this online document - the GridPP UserGuide - is to help new users join this community and access these resources to make a difference to the world beyond the realm of particle physics. The UserGuide is based on GitBook and may be found here: http://www.gridpp.ac.uk/userguid