698 results found
Rueckert D, Schnabel JA, 2014, Registration and segmentation in medical imaging, Studies in Computational Intelligence, Vol: 532, Pages: 137-156, ISSN: 1860-949X
The analysis of medical images plays an increasingly important role in many clinical applications. Different imaging modalities often provide complementary anatomical information about the underlying tissues such as the X-ray attenuation coefficients from X-ray computed tomography (CT), and proton density or proton relaxation times from magnetic resonance (MR) imaging. The images allow clinicians to gather information about the size, shape and spatial relationship between anatomical structures and any pathology, if present. Other imaging modalities provide functional information such as the blood flow or glucose metabolism from positron emission tomography (PET) or single-photon emission tomography (SPECT), and permit clinicians to study the relationship between anatomy and physiology. Finally, histological images provide another important source of information which depicts structures at a microscopic level of resolution. © 2014 Springer-Verlag Berlin Heidelberg.
Schirmer MD, Ball G, Counsell SJ, et al., 2014, Parcellation-independent multi-scale framework for brain network analysis, Pages: 23-32, ISSN: 1612-3786
© Springer International Publishing Switzerland 2014. Structural brain connectivity can be characterised by studies employing diffusion MR, tractography and the derivation of network measures. However, in some subject populations, such as neonates, the lack of a generally accepted paradigm for how the brain should be segmented or parcellated leads to the application of a variety of atlas- and random-based parcellation methods. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences has yet to be resolved, in order to enable more meaningful intraand inter-subject comparisons. This work proposes a parcellation-independent multi-scale analysis of commonly used network measures to describe changes in the brain. As an illustration, we apply our framework to a neonatal serial diffusion MRI data set and show its potential in characterising developmental changes. Furthermore, we use the measures provided by the framework to investigate the inter-dependence between network measures and apply an hierarchical clustering algorithm to determine a subset of measures for characterising the brain.
Wu X, Housden J, Ma Y, et al., 2014, A FAST CATHETER SEGMENTATION AND TRACKING FROM ECHOCARDIOGRAPHIC SEQUENCES BASED ON CORRESPONDING X-RAY FLUOROSCOPIC IMAGE SEGMENTATION AND HIERARCHICAL GRAPH MODELLING, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 951-954, ISSN: 1945-7928
Koikkalainen J, Lotjonen J, Ledig C, et al., 2014, AUTOMATIC QUANTIFICATION OF CT IMAGES FOR TRAUMATIC BRAIN INJURY, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 125-128, ISSN: 1945-7928
Baumgartner CE, Kolbitsch C, McClelland JR, et al., 2014, AUTOADAPTIVE MOTION MODELLING, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 457-460, ISSN: 1945-7928
Lotjonen J, Ledig C, Koikkalainen J, et al., 2014, EXTENDED BOUNDARY SHIFT INTEGRAL, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 854-857, ISSN: 1945-7928
Liu L, Shi W, Rueckert D, et al., 2014, CORONARY CENTERLINE EXTRACTION BASED ON OSTIUM DETECTION AND MODEL-GUIDED DIRECTIONAL MINIMAL PATH, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 133-136, ISSN: 1945-7928
Caballero J, Bai W, Price AN, et al., 2014, Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 106-+, ISSN: 0302-9743
Shi W, Lombaert H, Bai W, et al., 2014, Multi-atlas Spectral PatchMatch: Application to Cardiac Image Segmentation, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 348-+, ISSN: 0302-9743
Ledig C, Shi W, Bai W, et al., 2014, Patch-based Evaluation of Image Segmentation, 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 3065-3072, ISSN: 1063-6919
Gao Q, Asthana A, Tong T, et al., 2014, Hybrid Decision Forests for Prostate Segmentation in Multi-channel MR Images, 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 3298-3303, ISSN: 1051-4651
Guerrero R, Ledig C, Rueckert D, 2014, Manifold Alignment and Transfer Learning for Classification of Alzheimer's Disease, 5th International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 77-84, ISSN: 0302-9743
Koch LM, Wright R, Vatansever D, et al., 2014, Graph-Based Label Propagation in Fetal Brain MR Images, 5th International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 9-16, ISSN: 0302-9743
Ledig C, Shi W, Makropoulos A, et al., 2014, CONSISTENT AND ROBUST 4D WHOLE-BRAIN SEGMENTATION: APPLICATION TO TRAUMATIC BRAIN INJURY, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 673-676, ISSN: 1945-7928
Donoghue CR, Rao A, Bull AMJ, et al., 2014, LEARNING OSTEOARTHRITIS IMAGING BIOMARKERS USING LAPLACIAN EIGENMAP EMBEDDINGS WITH DATA FROM THE OAI, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1011-1014, ISSN: 1945-7928
Rao A, Ledig C, Newcombe V, et al., 2014, CONTUSION SEGMENTATION FROM SUBJECTS WITH TRAUMATIC BRAIN INJURY: A RANDOM FOREST FRAMEWORK, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 333-336, ISSN: 1945-7928
Bhatia KK, Price AN, Shi W, et al., 2014, SUPER-RESOLUTION RECONSTRUCTION OF CARDIAC MRI USING COUPLED DICTIONARY LEARNING, 11th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 947-950, ISSN: 1945-7928
Caballero J, Bai W, Price AN, et al., 2014, Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data., Med Image Comput Comput Assist Interv, Vol: 17, Pages: 106-113
Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation.
Shi W, Lombaert H, Bai W, et al., 2014, Multi-atlas spectral PatchMatch: application to cardiac image segmentation., Pages: 348-355
The automatic segmentation of cardiac magnetic resonance images poses many challenges arising from the large variation between different anatomies, scanners and acquisition protocols. In this paper, we address these challenges with a global graph search method and a novel spectral embedding of the images. Firstly, we propose the use of an approximate graph search approach to initialize patch correspondences between the image to be segmented and a database of labelled atlases, Then, we propose an innovative spectral embedding using a multi-layered graph of the images in order to capture global shape properties. Finally, we estimate the patch correspondences based on a joint spectral representation of the image and atlases. We evaluated the proposed approach using 155 images from the recent MICCAI SATA segmentation challenge and demonstrated that the proposed algorithm significantly outperforms current state-of-the-art methods on both training and test sets.
Pandit AS, Robinson E, Aljabar P, et al., 2014, Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth, Cerebral Cortex, Vol: 24, Pages: 2324-2333
Kainz B, Malamateniou C, Murgasova M, et al., 2014, Motion corrected 3D reconstruction of the fetal thorax from prenatal MRI., Med Image Comput Comput Assist Interv, Vol: 17, Pages: 284-291
In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature.
Ball G, Aljabar P, Zebari S, et al., 2014, Rich-club organization of the newborn human brain, Proceedings of the National Academy of Sciences, Vol: 111, Pages: 7456-7461
Wang Z, Bhatia K, Glocker B, et al., 2014, Geodesic Patch-based Segmentation, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Publisher: Springer, Pages: 666-673, ISSN: 0302-9743
Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.
Kainz B, Malamateniou C, Murgasova M, et al., 2014, Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI, Publisher: Springer International Publishing, Pages: 284-291
Pszczolkowski S, Zafeiriou S, Ledig C, et al., 2014, A Robust Similarity Measure for Nonrigid Image Registration with Outliers
Karim R, Housden RJ, Balasubramaniam M, et al., 2013, Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 15, ISSN: 1097-6647
Deligianni F, Varoquaux G, Thirion B, et al., 2013, A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 32, Pages: 2200-2214, ISSN: 0278-0062
Wolz R, Chu C, Misawa K, et al., 2013, Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 32, Pages: 1723-1730, ISSN: 0278-0062
Malcolme-Lawes LC, Juli C, Karim R, et al., 2013, Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: A 2-center study, HEART RHYTHM, Vol: 10, Pages: 1184-1191, ISSN: 1547-5271
Clerx L, van Rossum IA, Burns L, et al., 2013, Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment, NEUROBIOLOGY OF AGING, Vol: 34, Pages: 2003-2013, ISSN: 0197-4580
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