700 results found
Wu X, Housden J, Ma Y, et al., 2015, Fast catheter segmentation from echocardiographic sequences based on segmentation from corresponding x-ray fluoroscopy for cardiac catheterization interventions, IEEE Transactions on Medical Imaging, Vol: 34, Pages: 861-876, ISSN: 0278-0062
Bowles C, Nowlan NC, Hayat TTA, et al., 2015, Machine learning for the automatic localisation of foetal body parts in cine-MRI scans, Medical Imaging 2015: Image Processing, Publisher: Society of Photo-optical Instrumentation Engineers (SPIE), ISSN: 0277-786X
Kainz B, Steinberger M, Wein W, et al., 2015, Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
De Marvao A, Dawes TJ, Shi W, et al., 2015, Adverse changes in left ventricular structure begin at normotensive systolic blood pressures: A high resolution MRI study, Journal of Cardiovascular Magnetic Resonance, Pages: 1-2, ISSN: 1097-6647
Isgum I, Benders MJNL, Avants B, et al., 2015, Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge, MEDICAL IMAGE ANALYSIS, Vol: 20, Pages: 135-151, ISSN: 1361-8415
Rueckert D, Aljabar P, 2015, Non-rigid registration using free-form deformations, Handbook of Biomedical Imaging: Methodologies and Clinical Research, Pages: 277-294, ISBN: 9780387097480
© Springer Science+Business Media New York 2015. Free-form deformations are a powerful geometric modeling technique which can be used to represent complex 3D deformations. In recent years, freeform deformations have gained significant popularity in algorithms for the non-rigid registration of medical images. In this chapter we show how free-form deformations can be used in non-rigid registration to model complex local deformations of 3D organs. In particular, we discuss diffeomorphic and non-diffeomorphic representations of 3D deformation fields using free-form deformations as well as different penalty functions that can be used to constrain the deformation fields during the registration.We also show how free-form deformations can be used in combination with mutual information-based similarity metrics for the registration ofmono-modal and multi-modal medical images. Finally, we discuss applications of registration techniques based on free-form deformations for the analysis of images of the breast, heart and brain as well as for segmentation and shape modelling.
Karasawa K, Oda M, Hayashi Y, et al., 2015, Pancreas segmentation from 3D abdominal CT images using patient-specific weighted-subspatial probabilistic atlases, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Schuh A, Murgasova M, Makropoulos A, et al., 2015, Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration, 3rd International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA), Publisher: SPRINGER-VERLAG BERLIN, Pages: 27-37, ISSN: 0302-9743
Petitjean C, Zuluaga MA, Bai W, et al., 2015, Right ventricle segmentation from cardiac MRI: A collation study, MEDICAL IMAGE ANALYSIS, Vol: 19, Pages: 187-202, ISSN: 1361-8415
Bai W, Shi W, Ledig C, et al., 2015, Multi-atlas segmentation with augmented features for cardiac MR images, MEDICAL IMAGE ANALYSIS, Vol: 19, Pages: 98-109, ISSN: 1361-8415
Schmidt-Richberg A, Guerrero R, Ledig C, et al., 2015, Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease, Information processing in medical imaging : proceedings of the ... conference, Vol: 24, Pages: 387-398, ISSN: 1011-2499
The estimation of disease progression in Alzheimer's disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification.
Parisot S, Arslan S, Passerat-Palmbach J, et al., 2015, Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex, Information processing in medical imaging : proceedings of the ... conference, Vol: 24, Pages: 600-612, ISSN: 1011-2499
The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.
Arslan S, Parisot S, Rueckert D, 2015, Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI, Information processing in medical imaging : proceedings of the ... conference, Vol: 24, Pages: 85-97, ISSN: 1011-2499
Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.
Koch LM, Rajchl M, Tong T, et al., 2015, Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data, Information processing in medical imaging : proceedings of the ... conference, Vol: 24, Pages: 221-232, ISSN: 1011-2499
Manually annotating images for multi-atlas segmentation is an expensive and often limiting factor in reliable automated segmentation of large databases. Segmentation methods requiring only a proportion of each atlas image to be labelled could potentially reduce the workload on expert raters tasked with labelling images. However, exploiting such a database of partially labelled atlases is not possible with state-of-the-art multi-atlas segmentation methods. In this paper we revisit the problem of multi-atlas segmentation and formulate its solution in terms of graph-labelling. Our graphical approach uses a Markov Random Field (MRF) formulation of the problem and constructs a graph connecting atlases and the target image. This provides a unifying framework for label propagation. More importantly, the proposed method can be used for segmentation using only partially labelled atlases. We furthermore provide an extension to an existing continuous MRF optimisation method to solve the proposed problem formulation. We show that the proposed method, applied to hippocampal segmentation of 202 subjects from the ADNI database, remains robust and accurate even when the proportion of manually labelled slices in the atlases is reduced to 20%.
Baumgartner CF, Gomez A, Koch LM, et al., 2015, Self-aligning manifolds for matching disparate medical image datasets, Pages: 363-374, ISSN: 0302-9743
© Springer International Publishing Switzerland 2015. Manifold alignment can be used to reduce the dimensionality of multiple medical image datasets into a single globally consistent lowdimensional space. This may be desirable in a wide variety of problems, from fusion of different imaging modalities for Alzheimer’s disease classification to 4DMR reconstruction from 2D MR slices. Unfortunately, most existing manifold alignment techniques require either a set of prior correspondences or comparability between the datasets in high-dimensional space, which is often not possible. We propose a novel technique for the ‘self-alignment’ of manifolds (SAM) from multiple dissimilar imaging datasets without prior correspondences or inter-dataset image comparisons. We quantitatively evaluate the method on 4DMR reconstruction from realistic, synthetic sagittal 2D MR slices from 6 volunteers and real data from 4 volunteers. Additionally, we demonstrate the technique for the compounding of two free breathing 3D ultrasound views from one volunteer. The proposed method performs significantly better for 4DMR reconstruction than state-of-the-art image-based techniques.
Wang H, Shi W, Bai W, et al., 2015, Prediction of Clinical Information from Cardiac MRI Using Manifold Learning, 8th International Conference on Functional Imaging and Modeling of the Heart(FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 91-98, ISSN: 0302-9743
Oktay O, Gomez A, Keraudren K, et al., 2015, Probabilistic Edge Map (PEM) for 3D Ultrasound Image Registration and Multi-atlas Left Ventricle Segmentation, 8th International Conference on Functional Imaging and Modeling of the Heart(FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 223-230, ISSN: 0302-9743
Chen L, Tong T, Ho CP, et al., 2015, Identification of Cerebral Small Vessel Disease Using Multiple Instance Learning, MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I, Vol: 9349, Pages: 523-530, ISSN: 0302-9743
Bai W, Peressutti D, Oktay O, et al., 2015, Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+Normal Subjects, 8th International Conference on Functional Imaging and Modeling of the Heart(FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-9, ISSN: 0302-9743
Oktay O, Schuh A, Rajchl M, et al., 2015, Structured Decision Forests for Multi-modal Ultrasound Image Registration, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 363-371, ISSN: 0302-9743
Parisot S, Rajchl M, Passerat-Palmbach J, et al., 2015, A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INT PUBLISHING AG, Pages: 165-172, ISSN: 0302-9743
Bhatia KK, Caballero J, Price AN, et al., 2015, Fast Reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INT PUBLISHING AG, Pages: 510-518, ISSN: 0302-9743
Peressutti D, Bai W, Jackson T, et al., 2015, Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INT PUBLISHING AG, Pages: 493-500, ISSN: 0302-9743
Arslan S, Rueckert D, 2015, Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRI, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INT PUBLISHING AG, Pages: 47-54, ISSN: 0302-9743
Tong T, Gray K, Gao Q, et al., 2015, Nonlinear graph fusion for multi-modal classification of Alzheimer’s disease, Pages: 77-84, ISSN: 0302-9743
© Springer International Publishing Switzerland 2015. Recent studies have demonstrated that biomarkers from multiple modalities contain complementary information for the diagnosis of Alzheimer’s disease (AD) and its prodromal stage mild cognitive impairment (MCI). In order to fuse data from multiple modalities, most previous approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. However, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the weights assigned to each modality. In this paper, we propose a nonlinear graph fusion method to efficiently exploit the complementarity in the multi-modal data for the classification of AD. Specifically, a graph is first constructed for each modality individually. Afterwards, a single unified graph is obtained via a nonlinear combination of the graphs in an iterative cross diffusion process. Using the unified graphs, we achieved classification accuracies of 91.8% between AD subjects and normal controls (NC), 79.5% between MCI subjects and NC and 60.2% in a three-way classification, which are competitive with state-of-the-art results.
Guerrero R, Ledig C, Schmidt-Richberg A, et al., 2015, Group-constrained Laplacian Eigenmaps: Longitudinal AD biomarker learning, Pages: 178-185, ISSN: 0302-9743
© Springer International Publishing Switzerland 2015. Longitudinal modeling of biomarkers to assess a subject’s risk of developing Alzheimers disease (AD) or determine the current state in the disease trajectory has recently received increased attention. Here, a new method to estimate the time-to-conversion (TTC) of mild cognitive impaired (MCI) subjects to AD from a low-dimensional representation of the data is proposed. This is achieved via a combination of multi-level feature selection followed by a novel formulation of the Laplacian Eigenmaps manifold learning algorithm that allows the incorporation of group constraints. Feature selection is performed using Magnetic Resonance (MR) images that have been aligned at different detail levels to a template. The suggested group constraints are added to the construction of the neighborhood matrix which is used to calculate the graph Laplacian in the Laplacian Eigenmaps algorithm. The proposed formulation yields relevant improvements for the prediction of the TTC and for the three-way classification (control/MCI/AD) on the ADNI database.
Wu G, Coupé P, Zhan Y, et al., 2015, Patch-Based Techniques in Medical Imaging: First International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015 Munich, Germany, October 9, 2015 Revised Selected Papers, ISSN: 0302-9743
Campos S, Pizarro L, Valle C, et al., 2015, Evaluating Imputation Techniques for Missing Data in ADNI: A Patient Classification Study, 20th Iberoamerican Congress on Pattern Recognition (CIARP), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 3-10, ISSN: 0302-9743
Xavier I, Pereira M, Giraldi G, et al., 2015, A Photo-Realistic Generator of Most Expressive and Discriminant Changes in 2D Face Images, 6th International Conference on Emerging Security Technologies (EST), Publisher: IEEE, Pages: 80-85
Maas AIR, Menon DK, Steyerberg EW, et al., 2015, Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study., Neurosurgery, Vol: 76, Pages: 67-80
BACKGROUND: Current classification of traumatic brain injury (TBI) is suboptimal, and management is based on weak evidence, with little attempt to personalize treatment. A need exists for new precision medicine and stratified management approaches that incorporate emerging technologies. OBJECTIVE: To improve characterization and classification of TBI and to identify best clinical care, using comparative effectiveness research approaches. METHODS: This multicenter, longitudinal, prospective, observational study in 22 countries across Europe and Israel will collect detailed data from 5400 consenting patients, presenting within 24 hours of injury, with a clinical diagnosis of TBI and an indication for computed tomography. Broader registry-level data collection in approximately 20,000 patients will assess generalizability. Cross sectional comprehensive outcome assessments, including quality of life and neuropsychological testing, will be performed at 6 months. Longitudinal assessments will continue up to 24 months post TBI in patient subsets. Advanced neuroimaging and genomic and biomarker data will be used to improve characterization, and analyses will include neuroinformatics approaches to address variations in process and clinical care. Results will be integrated with living systematic reviews in a process of knowledge transfer. The study initiation was from October to December 2014, and the recruitment period was for 18 to 24 months. EXPECTED OUTCOMES: Collaborative European NeuroTrauma Effectiveness Research in TBI should provide novel multidimensional approaches to TBI characterization and classification, evidence to support treatment recommendations, and benchmarks for quality of care. Data and sample repositories will ensure opportunities for legacy research. DISCUSSION: Comparative effectiveness research provides an alternative to reductionistic clinical trials in restricted patient populations by exploiting differences in biology, care, and outcome to support
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