693 results found
Makropoulos A, Aljabar P, Wright R, et al., 2015, Regional growth and atlasing of the developing human brain, Neuroimage, Vol: 125, Pages: 456-478, ISSN: 1095-9572
Detailed morphometric analysis of the neonatal brain is required to characterise brain development and define neuroimaging biomarkers related to impaired brain growth. Accurate automatic segmentation of neonatal brain MRI is a prerequisite to analyse large datasets. We have previously presented an accurate and robust automatic segmentation technique for parcellating the neonatal brain into multiple cortical and subcortical regions. In this study, we further extend our segmentation method to detect cortical sulci and provide a detailed delineation of the cortical ribbon. These detailed segmentations are used to build a 4-dimensional spatio-temporal structural atlas of the brain for 82 cortical and subcortical structures throughout this developmental period. We employ the algorithm to segment an extensive database of 420 MR images of the developing brain, from 27 to 45. weeks post-menstrual age at imaging. Regional volumetric and cortical surface measurements are derived and used to investigate brain growth and development during this critical period and to assess the impact of immaturity at birth. Whole brain volume, the absolute volume of all structures studied, cortical curvature and cortical surface area increased with increasing age at scan. Relative volumes of cortical grey matter, cerebellum and cerebrospinal fluid increased with age at scan, while relative volumes of white matter, ventricles, brainstem and basal ganglia and thalami decreased. Preterm infants at term had smaller whole brain volumes, reduced regional white matter and cortical and subcortical grey matter volumes, and reduced cortical surface area compared with term born controls, while ventricular volume was greater in the preterm group. Increasing prematurity at birth was associated with a reduction in total and regional white matter, cortical and subcortical grey matter volume, an increase in ventricular volume, and reduced cortical surface area.
de Marvao A, Dawes TJW, Shi W, et al., 2015, Precursors of the hypertensive heart phenotype develop in normotensive adults: a high resolution 3D MRI study, JACC: Cardiovascular Imaging, Vol: 8, Pages: 1260-1269, ISSN: 1936-878X
ObjectivesThis study used high-resolution 3-dimensional cardiac magnetic resonance to define the anatomical and functional left ventricular (LV) properties associated with increasing systolic blood pressure (SBP) in a drug-naïve cohort.BackgroundLV hypertrophy and remodeling occur in response to hemodynamic stress but little is known about how these phenotypic changes are initiated in the general population.MethodsIn this study, 1,258 volunteers (54% women, mean age 40.6 ± 12.8 years) without self-reported cardiovascular disease underwent 3-dimensional cardiac magnetic resonance combined with computational modeling. The relationship between SBP and wall thickness (WT), relative WT, end-systolic wall stress (WS), and fractional wall thickening were analyzed using 3-dimensional regression models adjusted for body surface area, sex, race, age, and multiple testing. Significantly associated points in the LV model (p < 0.05) were identified and the relationship with SBP reported as mean β coefficients.ResultsThere was a continuous relationship between SBP and asymmetric concentric hypertrophic adaptation of the septum and anterior wall that was associated with normalization of wall stress. In the lateral wall an increase in wall stress with rising SBP was not balanced by a commensurate hypertrophic relationship. In normotensives, SBP was positively associated with WT (β = 0.09) and relative WT (β = 0.07) in the septal and anterior walls, and this regional hypertrophic relationship was progressively stronger among pre-hypertensives (β = 0.10) and hypertensives (β = 0.30).ConclusionsThese findings show that the precursors of the hypertensive heart phenotype can be traced to healthy normotensive adults and that an independent and continuous relationship exists between adverse LV remodeling and SBP in a low-risk population. These adaptations show distinct regional variations with concentric hypertrophy of the septum and eccentric hyper
Keraudren K, Kainz B, Oktay O, et al., 2015, Automated localization of fetal organs in MRI using random forests with steerable features, Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015, Publisher: Springer, Pages: 620-627, ISSN: 0302-9743
Fetal MRI is an invaluable diagnostic tool complementary to ultrasound thanks to its high contrast and resolution. Motion artifacts and the arbitrary orientation of the fetus are two main challenges of fetal MRI. In this paper, we propose a method based on Random Forests with steerable features to automatically localize the heart, lungs and liver in fetal MRI. During training, all MR images are mapped into a standard coordinate system that is defined by landmarks on the fetal anatomy and normalized for fetal age. Image features are then extracted in this coordinate system. During testing, features are computed for different orientations with a search space constrained by previously detected landmarks. The method was tested on healthy fetuses as well as fetuses with intrauterine growth restriction (IUGR) from 20 to 38 weeks of gestation. The detection rate was above 90% for all organs of healthy fetuses in the absence of motion artifacts. In the presence of motion, the detection rate was 83% for the heart, 78% for the lungs and 67% for the liver. Growth restriction did not decrease the performance of the heart detection but had an impact on the detection of the lungs and liver. The proposed method can be used to initialize subsequent processing steps such as segmentation or motion correction, as well as automatically orient the 3D volume based on the fetal anatomy to facilitate clinical examination.
Zimmer V, Glocker B, Aljabar P, et al., 2015, Learning and combining image similarities for neonatal brain population studies, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer International Publishing, Pages: 110-117, ISSN: 0302-9743
The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.
Kanavti F, Tong T, Misawa K, et al., 2015, Supervoxel classification forests for estimating pairwise image correspondences, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer International Publishing, Pages: 94-101, ISSN: 0302-9743
This paper proposes a general method for establishing pairwise correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxelwise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling which is then regularized using majority voting within the boundaries of the target’s supervoxels. This yields semi-dense correspondences in a fully automatic, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as, atlas/patch-based segmentation, registration, and atlas construction. Our approach is evaluated on a set of 150 abdominal CT images. In this dataset we use manual organ segmentations for quantitative evaluation. In particular, the quality of the correspondences is determined in a label propagation setting. Comparison to other state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.
Bai W, Shi W, de Marvao A, et al., 2015, A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion, Medical Image Analysis, Vol: 26, Pages: 133-145, ISSN: 1361-8423
Atlases encode valuable anatomical and functional information from a population. In this work, a bi-ventricular cardiac atlas was built from a unique data set, which consists of high resolution cardiac MR images of 1000+ normal subjects. Based on the atlas, statistical methods were used to study the variation of cardiac shapes and the distribution of cardiac motion across the spatio-temporal domain. We have shown how statistical parametric mapping (SPM) can be combined with a general linear model to study the impact of gender and age on regional myocardial wall thickness. Finally, we have also investigated the influence of the population size on atlas construction and atlas-based analysis. The high resolution atlas, the statistical models and the SPM method will benefit more studies on cardiac anatomy and function analysis in the future.
Cardoso MJ, Modat M, Wolz R, et al., 2015, Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion, IEEE Transactions on Medical Imaging, Vol: 34, Pages: 1976-1988, ISSN: 1558-254X
Clinical annotations, such as voxel-wise binary orprobabilistic tissue segmentations, structural parcellations, pathologicalregions-of-interest and anatomical landmarks are key tomany clinical studies. However, due to the time consuming natureof manually generating these annotations, they tend to be scarceand limited to small subsets of data. This work explores a novelframework to propagate voxel-wise annotations between morphologicallydissimilar images by diffusing and mapping the availableexamples through intermediate steps. A spatially-variant graphstructure connecting morphologically similar subjects is introducedover a database of images, enabling the gradual diffusion ofinformation to all the subjects, even in the presence of large-scalemorphological variability. We illustrate the utility of the proposedframework on two example applications: brain parcellation usingcategorical labels and tissue segmentation using probabilistic features.The application of the proposed method to categorical labelfusion showed highly statistically significant improvements whencompared to state-of-the-art methodologies. Significant improvementswere also observed when applying the proposed frameworkto probabilistic tissue segmentation of both synthetic and real data,mainly in the presence of large morphological variability.
Kamnitsas K, Chen L, Ledig C, et al., 2015, Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI, MICCAI Brain Lesion Workshop 2015
Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2015, Segmentation of Traumatic Brain Injuries with Convolutional Neural Networks, 2nd Turku Traumatic Brain Injury Symposium
Heckemann RA, Ledig C, Gray KR, et al., 2015, Brain Extraction Using Label Propagation and Group Agreement: Pincram (vol 10, e0129211, 2015), PLOS ONE, Vol: 10, ISSN: 1932-6203
Corden B, De Marvao ASM, Dawes TJW, et al., 2015, Left ventricular remodelling with increasing body fat: a 3D cardiac phenotyping study, Congress of the European-Society-of-Cardiology (ESC), Publisher: Oxford University Press (OUP), Pages: 118-118, ISSN: 1522-9645
De Marvao A, Dawes T, Shi W, et al., 2015, Rising systolic blood pressure leads to a continuous progression towards hypertensive heart disease: a prospective population study, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 191-191, ISSN: 0195-668X
Dawes T, De Marvao A, Shi W, et al., 2015, Prognostic value of right heart adaptation to pulmonary arterial hypertension: a prospective cohort study, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 708-709, ISSN: 0195-668X
Heckemann RA, Ledig C, Gray KR, et al., 2015, Brain Extraction Using Label Propagation and Group Agreement: Pincram, PLOS One, Vol: 10, ISSN: 1932-6203
Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisitefor many neuroimaging methods. Most existing methods exhibit disadvantages inthat they are laborious, yield inconsistent results, and/or require training data to closelymatch the data to be processed. Here, we present pincram, an automatic, versatile methodfor accurately labelling the adult brain on T1-weighted 3D MR head images. The methoduses an iterative refinement approach to propagate labels from multiple atlases to a giventarget image using image registration. At each refinement level, a consensus label is generated.At the subsequent level, the search for the brain boundary is constrained to the neighbourhoodof the boundary of this consensus label. The method achieves high accuracy(Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of >0.97) and performs better than many state-of-the-art methods as evidenced by independentevaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, theprogram generates the "success index," a scalar metadatum indicative of the accuracy ofthe output label. Pincram is available as open source software.
Zhuang X, Bai W, Song J, et al., 2015, Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection, MEDICAL PHYSICS, Vol: 42, Pages: 3822-3833, ISSN: 0094-2405
Arslan S, Parisot S, Rueckert D, 2015, Supervertex clustering of the cerebral cortex using resting-state fMRI, Organization for Human Brain Mapping (OHBM) Annual Meeting 2015
Rueckert D, Wright R, Makropoulos A, et al., 2015, Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: application of spectral surface matching, Neuroimage, Vol: 120, Pages: 467-480, ISSN: 1095-9572
In this study, we construct a spatio-temporal surface atlas of the developing cerebral cortex, which is an important tool for analysing and understanding normal and abnormal cortical development. In utero Magnetic Resonance Imaging (MRI) of 80 healthy foetuses was performed, with a gestational age range of 21.7 to 38.9 weeks. Topologically correct cortical surface models were extracted from reconstructed 3D MRI volumes. Accurate correspondences were obtained by applying a joint spectral analysis to cortices for sets of subjects close to a specific age. Sulcal alignment was found to be accurate in comparison to spherical demons, a state of the art registration technique for aligning 2D cortical representations (average Fréchet distance ≈ 0.4 mm at 30 weeks). We construct consistent, unbiased average cortical surface templates, for each week of gestation, from age-matched groups of surfaces by applying kernel regression in the spectral domain. These were found to accurately capture the average cortical shape of individuals within the cohort, suggesting a good alignment of cortical geometry. Each spectral embedding and its corresponding cortical surface template provide a dual reference space where cortical geometry is aligned and a vertex-wise morphometric analysis can be undertaken.
Braga RM, Roze E, Ball G, et al., 2015, Development of the Corticospinal and Callosal Tracts from Extremely Premature Birth up to 2 Years of Age, PLOS One, Vol: 10, ISSN: 1932-6203
White matter tracts mature asymmetrically during development, and this development canbe studied using diffusion magnetic resonance imaging. The aims of this study were i. togenerate dynamic population-averaged white matter registration templates covering in detailthe period from 25 weeks gestational age to term, and extending to 2 years of age basedon DTI and fractional anisotropy, ii. to produce tract-specific probability maps of the corticospinaltracts, forceps major and forceps minor using probabilistic tractography, and iii. toassess the development of these tracts throughout this critical period of neurodevelopment.We found evidence for asymmetric development across the fiber bundles studied, with thecorticospinal tracts showing earlier maturation (as measured by fractional anisotropy) butslower volumetric growth compared to the callosal fibers. We also found evidence for an anteriorto posterior gradient in white matter microstructure development (as measured bymean diffusivity) in the callosal fibers, with the posterior forceps major developing at a fasterrate than the anterior forceps minor in this age range. Finally, we report a protocol for delineatingcallosal and corticospinal fibers in extremely premature cohorts, and make availablepopulation-averaged registration templates and a probabilistic tract atlas which we hope willbe useful for future neonatal and infant white-matter imaging studies.
Tong T, Wolz R, Wang Z, et al., 2015, Discriminative dictionary learning for abdominal multi-organ segmentation., Medical Image Analysis, Vol: 23, Pages: 92-104, ISSN: 1361-8423
An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
Newcombe VFJ, Correia MM, Ledig C, et al., 2015, Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI: A Diffusion Tensor Imaging Study, Neurorehabilitation and Neural Repair, Vol: 30, Pages: 49-62, ISSN: 1552-6844
Objective. Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. Methods. Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. Results. Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to years postictus. Changes in diffusion tensor imaging were driven by increases in both axial and radial diffusivity except for the earliest time point, and were associated with changes in reaction time and performance in a visual memory and learning task (paired associates learning). Dynamic structural changes after TBI can be detected using diffusion tensor imaging and could explain changes in behavior. Conclusions. These data can provide further insight into early and late pathophysiology, and begin to provide a framework that allows magnetic resonance imaging to be used as an imaging biomarker of therapy response. Knowledge of the temporal pattern of changes in TBI patient populations also provides a contextual framework for assessing imaging changes in individuals at any given time point.
Vasylechko S, Malamateniou C, Nunes RG, et al., 2015, T2*Relaxometry of Fetal Brain at 1.5 Tesla Using a Motion Tolerant Method, MAGNETIC RESONANCE IN MEDICINE, Vol: 73, Pages: 1795-1802, ISSN: 0740-3194
PurposeThe aim of this study was to determine T2* values for the fetal brain in utero and to compare them with previously reported values in preterm and term neonates. Knowledge of T2* may be useful for assessing brain development, brain abnormalities, and for optimizing functional imaging studies.MethodsMaternal respiration and unpredictable fetal motion mean that conventional multishot acquisition techniques used in adult T2* relaxometry studies are not practical. Single shot multiecho echo planar imaging was used as a rapid method for measuring fetal T2* by effectively freezing intra-slice motion.ResultsT2* determined from a sample of 24 subjects correlated negatively with gestational age with mean values of 220 ms (±45) for frontal white matter, 159 ms (±32) for thalamic gray matter, and 236 ms (±45) for occipital white matter.ConclusionFetal T2* values are higher than those previously reported for preterm neonates and decline with a consistent trend across gestational age. The data suggest that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI to reach optimal BOLD sensitivity.
Kainz B, Malamateniou C, Ferrazzi G, et al., 2015, Adaptive scan strategies for fetal MRI imaging using slice to volume techniques, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 849-852, ISSN: 1945-7928
In this paper several novel methods to account for fetal movements during fetal Magnetic Resonance Imaging (fetal MRI) are explored. We show how slice-to-volume reconstruction methods can be used to account for motion adaptively during the scan. Three candidate methods are tested for their feasibility and integrated into a computer simulation of fetal MRI. The first alters the main orientation of the stacks used for reconstruction, the second stops if too much motion occurs during slice acquisition and the third steers the orientation of each slice individually. Reconstruction informed adaptive scanning can provide a peak signal-to-noise ratio (PSNR) improvement of up to 2 dB after only two stacks of scanned slices and is more efficient with respect to the uncertainty of the final reconstruction.
Ledig C, Heckemann RA, Hammers A, et al., 2015, Robust whole-brain segmentation: Application to traumatic brain injury, MEDICAL IMAGE ANALYSIS, Vol: 21, Pages: 40-58, ISSN: 1361-8415
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
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