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Ben glocker phd thesis

ben glocker phd thesis

The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88 and 99 for ben glocker phd thesis heart coverage estimation and 85 and 95 for motion detection allowing. We further evaluate the neurobiological relevance of the identified regions based on evidence from large-scale UK Biobank studies. Slides from my thesis defence. Rueckert, Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging, plos One, 2017 Hideaki Suzuki, He Gao, Wenjia Bai, Evangelos Evangelou, Ben Glocker, Declan. Newcombe, Hyponatraemic and hypo-osmotic states on admission are associated with increased contusion and oedema measured on MR imaging, 12th Symposium of the (ints), 2016 Chen Qin, Ricardo Guerrero Moreno, Christopher Bowles, Christian Ledig, Philip Scheltens, Frederik Barkhof, Hanneke Rhodius-Meester, Betty Tijms, Afina. Furthermore, there is mounting evidence that accurate segmentation of thevarious tumor sub-regions can offer the basis for quantitative image analysistowards prediction of patient overall survival. Li, Evaluation and Comparison of 3D Intervertebral Disc Localization and Segmentation Methods for 3D T2 MR Data: A Grand Challenge, Medical image analysis, 2017 Oktay O, Bai W, Guerrero R, Rajchl M, de Marvao A, O'Regan D, Cook. Rueckert, Semi-Supervised Learning for Network-Based Cardiac MR Image Segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention (miccai), 2017 Alessandro Vandini, Ben Glocker, Mohamad Hamady, Guang-Zhong Yang, Robust guidewire tracking under large deformations combining segment-like features, Medical Image Analysis, 2017. By combining FCN with alarge-scale annotated dataset, the proposed automated method achieves a highperformance on par with human experts in segmenting the LV and RV on short-axisCMR images and the left atrium (LA) and right atrium (RA) on long-axis CMRimages. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy.53mm,.98mm and.84mm, respectively. The source code for the proposed AG models is publicly available. Glocker, Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases, miccai Workshop on Medical Computer Vision: Algorithms for Big Data, 2016.

Ben, glocker, phD, imperial College London, London ResearchGate

Conference paper, this work investigates continual learning of two segmentation tasks in brain MRIwith neural networks. The performance of the method has beenevaluated using a number of technical metrics, including the Dice metric, meancontour distance and Hausdorff distance, as well as clinically relevantmeasures, including left ventricle (LV) end-diastolic volume (lvedv) andend-systolic volume (lvesv LV mass (LVM right ventricle. Glocker, Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation, miccai Brain Lesion Workshop, 2017. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Kainz, Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion, International Conference on Medical Image Computing and Computer Assisted Intervention (miccai), 2017. Lee, Hugh Salimbeni, Marc. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). Munier, Daniel Rueckert, Jean-Philippe Thiran, Meritxell Bach Cuadra, Raphael Sznitman, Multi-channel MRI Segmentation of Eye Structures and Tumors using Patient-specific Features, PLoS One, 2017 Nick Pawlowski, Miguel Jaques, Ben Glocker, Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection, iclr Workshop, 2017.

Loeckx, Gang Song,. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz, Image-Based Registration in Canonical Atlas Space, International Conference on Medical Imaging with Deep Learning (midl abstract track, non-archival, 2018 Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew Lee, Amir Alansary, Steven McDonagh, Joseph Hajnal. A recent research direction for the localization of anatomical landmarks with learning-based methods is to explore ways to enrich the trained models with context information. Cardiovascular magnetic resonance (CMR) imaging is a standard imagingmodality for assessing cardiovascular diseases (CVDs the leading cause ofdeath globally. Specifically, we introduce the concept of template transformer networks where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. The proposed method detects these landmarks with a mean localization error.0. Graphs are widely used as a natural framework that captures interactionsbetween individual elements represented as nodes in a graph. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. In a multi-centre study with brain MRI data, we show that the proposed technique produces excellent correspondence between the matched densities and histograms.

Ben, glocker GlockerBen) Twitter

Request URL: Request URI: Query String). We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. This thesis will explore the possibilities of convex optimization for metric r 23, 2012, benjamin John Sapp,. Alex Kulesza and Ben Taskar.Nov 18, 2013 Ben Taskar, a rising star in the University of Washington's Department of Taskar, who earned a bachelor's and doctoral degree from Stanford . Glocker, Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation, International Workshop on Machine Learning in Medical Imaging (mlmi), (20) Ben Glocker, Nikos Paragios, Ramin Zabih, Discrete Graphical Models in Biomedical Image Analysis, Special Issue Medical Image Analysis, volume 27, 2016. ORegan, Paul Elliott, Paul. We propose a novel Bayesian nonparametric method to learntranslation-invariant relationships on non-Euclidean domains. The approach isshowcased on subcortical structures. Price, Tammy Riklin Raviv, Syed. Criminisi, Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos, International Conference on Medical Image Computing and Computer Assisted Intervention (miccai), 2014 Renato Salas-Moreno, Ben Glocker, Paul Kelly, Andrew Davison, Dense Planar slam, ieee International Symposium on Mixed. In this paper, we propose a general Riemannian formulation of the pose estimation problem, and train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric. Our findings show this recent methodreduces catastrophic forgetting, while large room for improvement exists in thesechallenging settings for continual learning. Manual annotation is costly and time consuming if it has to becarried out for every new target domain.

BenGlockerTalk2011 Dissertation Defense

Dissertation Proposal Research Timetable, Ben Taskar Phd Thesis.Business plan writing service. Applications includesurface fairing - flowing a mesh onto say, a reference sphere - and meshextrusion -.g., rebuilding a complex shape from a reference sphere andcurvature specification. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric. We introduce a novel intensity normalisation scheme based on density matching, wherein the histograms are modelled as Dirichlet process Gaussian mixtures. Click here click here click here click here click here. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables ben glocker phd thesis us to discriminate between successful and failed segmentations on a per-cases basis. However, for years, clinicians have beenrelying on manual approaches for CMR image analysis, which is time consumingand prone to subjective errors. The structure of these models allows for high dimensional inputs whileretaining expressibility, as is the case with convolutional neural networks.

Lee, Amir Alansary, Steven McDonagh,. Van der Flier, Ben Glocker, Daniel Rueckert, A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation, miccai Workshop on Machine Learning in Medical Imaging, 2016 Whelan T, Salas-Moreno RF, Glocker B, Davison AJ, Leutenegger S, ElasticFusion: Real-Time Dense slam and Light. Statistical Relational n received his. As initial validation, we present data for 400 scans demonstrating 99 accuracy for classifying low and high quality segmentations using predicted DSC scores. Glocker, Automatic Quality Control of Cardiac MRI Segmentation in Large-scale Population Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention (miccai), 2017. Verified email at D Anguelov, B Taskar, V Chatalbashev, D Koller, D Gupta, G Heitz,.

Doctor of, philosophy - Wikipedia

Driven by this, we explore GCNs for the task of ROI identification and propose a ben glocker phd thesis visual attribution method based on class activation mapping. Lee, Valentina Carapella, Young Jin Kim, Stefan. The source mixture model is transformed to minimise its L2 divergence towards a target model, then the voxel intensities are transported through a mass-conserving flow to maintain agreement with the moving density. CMR enables accurate quantification of the cardiac chambervolume, ejection fraction and myocardial mass, providing information fordiagnosis and monitoring of CVDs. Conference paper, the variations in multi-center data in medical imaging studies have broughtthe necessity of domain adaptation. Thesis, New York University, 2016. Deisenroth, Ben Glocker, Patch kernels for Gaussian processes in high-dimensional imaging problems, nips Workshop on Practical Bayesian Nonparametrics, 2016 Emma. Glocker, Automatic TBI Lesion Segmentation in Anisotropic CT using Convolutional Neural Networks, Frontiers in Traumatic Brain Injury, 2017 Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AG, Fully automatic, multi-organ segmentation.

Ben, glocker - Semantic Scholar

Conference paper, conference paper, journal article, journal article, journal article. Newcombe, Acute MRI enhances prognostication in traumatic brain injury, Intensive Care Society, State of the Art Meeting, 2015 Kanavati F, Tong T, Misawa K, Mori K, Rueckert D, Glocker B, Supervoxel Classification Forests for Estimating Pairwise Image Correspondences, International Workshop. However, the underlying assumption of many analysis techniques that corresponding tissues have consistent intensities in all images is often violated in multi-centre databases. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Journal article, neuroNet is a deep convolutional neural network mimicking multiple popularand state-of-the-art brain segmentation tools including FSL, SPM, and e network is trained on 5,000 T1-weighted brain MRI scans from the UK BiobankImaging Study that have been automatically segmented into. Predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3 is an omnipresent problem in medical image analysis. Deep learning methods often parameterise poses with a representation that separates rotation and translation. Staring, Accuracy Estimation for Medical Image Registration Using Regression Forests, International Conference on Medical Image Computing and Computer Assisted Intervention (miccai), 2016.

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. Lee, Martin Rajchl, Steven McDonagh, Enzo Ferrante, Konstantinos Kamnitsas, Sam Cooke, Susan Stevenson, Aneesh Khetani, Tom Newman, Fred Zeiler, Richard Digby, Jonathan. The performance of our implementations is demonstrated on several clinical applications including atlas generation, multi-modal brain registration, automatic segmentation via atlas-matching, and whole-body MRI stitching, as well as for the non-clinical problem of optical flow estimation. To explore in this context the capabilities of current methodsfor countering catastrophic forgetting of the first task when a new one is learned, we investigateelastic weight consolidation1, a recently proposed method basedon Fisher information, originally evaluated on reinforcement learning of Atarigames. J., Babu-Narayan., Firmin. Peroni, Rui Li,.C. AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. (computer science) and students, especially Kathrin Probst, Alicia Tribble, Rosie Jones and Ben Han. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. Coles, Daniel Rueckert, David.