101 results found
Gryska EA, Schneiderman J, Heckemann RA, 2019, Automatic brain lesion segmentation on standard MRIs of the human head: a scoping review protocol, BMJ Open, Vol: 9, ISSN: 2044-6055
INTRODUCTION: Automatic brain lesion segmentation from medical images has great potential to support clinical decision making. Although numerous methods have been proposed, significant challenges must be addressed before they will become established in clinical and research practice. We aim to elucidate the state of the art, to provide a synopsis of competing approaches and identify contrasts between them. METHODS AND ANALYSIS: We present the background and study design of a scoping review for automatic brain lesion segmentation methods for conventional MRI according to the framework proposed by Arksey and O'Malley. We aim to identify common image processing steps as well as mathematical and computational theories implemented in these methods. We will aggregate the evidence on the efficacy and identify limitations of the approaches. Methods to be investigated work with standard MRI sequences from human patients examined for brain lesions, and are validated with quantitative measures against a trusted reference. PubMed, IEEE Xplore and Scopus will be searched using search phrases that will ensure an inclusive and unbiased overview. For matching records, titles and abstracts will be screened to ensure eligibility. Studies will be excluded if a full paper is not available or is not written in English, if non-standard MR sequences are used, if there is no quantitative validation, or if the method is not automatic. In the data charting phase, we will extract information about authors, publication details and study cohort. We expect to find information about preprocessing, segmentation and validation procedures. We will develop an analytical framework to collate, summarise and synthesise the data. ETHICS AND DISSEMINATION: Ethical approval for this study is not required since the information will be extracted from published studies. We will submit the review report to a peer-reviewed scientific journal and explore other venues for presenting the work.
Prange S, Metereau E, Maillet A, et al., 2019, Microstructural changes in white and grey matter related to apathy, depression and anxiety in de novo Parkinson's disease patients, 31st Congress of the European-College-of-Neuropsychopharmacology (ECNP), Publisher: ELSEVIER SCIENCE BV, Pages: S161-S161, ISSN: 0924-977X
Andersson KME, Wasen C, Juzokaite L, et al., 2018, Inflammation in the hippocampus affects IGF1 receptor signaling and contributes to neurological sequelae in rheumatoid arthritis, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 115, Pages: E12063-E12072, ISSN: 0027-8424
Prange S, Metereau E, Maillet A, et al., 2018, Microstructural changes in white and grey matter related to apathy, depression and anxiety in de novo Parkinson's disease patients, International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, Pages: S674-S674, ISSN: 0885-3185
Ledig C, Schuh A, Guerrero, et al., 2018, Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, Vol: 8, ISSN: 2045-2322
Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
Andersson KM, Wasen C, Silfversward ST, et al., 2018, Physical functioning in rheumatoid arthritis is controlled by hippocampus and insulin-like growth factor-1 receptor signalling, Publisher: TAYLOR & FRANCIS LTD, Pages: 28-28, ISSN: 0300-9742
Wild HM, Heckemann RA, Studholme C, et al., 2017, Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases., PLoS ONE, Vol: 12, ISSN: 1932-6203
Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlas
Faillenot I, Heckemann RA, Frot M, et al., 2017, Macroanatomy and 3D probabilistic atlas of the human insula., Neuroimage, Vol: 150, Pages: 88-98
The human insula is implicated in numerous functions. More and more neuroimaging studies focus on this region, however no atlas offers a complete subdivision of the insula in a reference space. The aims of this study were to define a protocol to subdivide insula, to create probability maps in the MNI152 stereotaxic space, and to provide normative reference volume measurements for these subdivisions. Six regions were manually delineated bilaterally on 3D T1 MR images of 30 healthy subjects: the three short gyri, the anterior inferior cortex, and the two long gyri. The volume of the insular grey matter was 7.7 ± 0.9cm3 in native space and 9.9 ± 0.6cm3 in MNI152 space. These volumes expressed as a percentage of the ipsilateral grey matter volume were minimally larger in women (2.7±0.2%) than in men (2.6±0.2%). After spatial normalization, a stereotactic probabilistic atlas of each subregion was produced, as well as a maximum-probability atlas taking into account surrounding structures. Automatically labelling insular subregions via a multi-atlas propagation and label fusion strategy (MAPER) in a leave-one-out experiment showed high spatial overlaps of such automatically defined insular subregions with the manually derived ones (mean Jaccard index 0.65, corresponding to a mean Dice index of 0.79), with an average mean volume error of 2.6%. Probabilistic and maximum probability atlases and the original delineations are available on the web under free academic licences.
Merida I, Reilhac A, Redoute J, et al., 2017, Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR, PHYSICS IN MEDICINE AND BIOLOGY, Vol: 62, Pages: 2834-2858, ISSN: 0031-9155
Rosen J, Krysl D, Strandberg J, et al., 2016, THREE-DIMENSIONAL VISUALIZATION OF MORPHOMETRIC BRAIN ANOMALIES IN PATIENTS WITH TEMPORAL LOBE EPILEPSY, Publisher: WILEY, Pages: 224-224, ISSN: 0013-9580
Scott GPT, Ramlackhansingh A, Edison P, et al., 2016, Amyloid pathology and axonal injury after brain trauma, Neurology, Vol: 86, Pages: 821-828, ISSN: 0028-3878
Objective: To image amyloid-β (Aβ) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and Aβ are correlated, and compare the spatial distribution of Aβ to Alzheimer’s disease.Methods: Patients 11 months to 17 years after moderate-severe TBI had 11C-Pittsburgh compound-B (PIB) PET, structural and diffusion MRI and neuropsychological examination. Healthy aged controls and AD patients had PET and structural MRI. Binding potential (BPND) images of 11C-PIB, which index Aβ plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy (FA), was estimated and correlated with 11C-PIB BPND.Results: 28 participants (9 TBI, 9 controls, 10 AD) were assessed. Increased 11C-PIB BPND was found in TBI versus controls in the posterior cingulate cortex (PCC) and cerebellum. Binding in the PCC increased with decreasing FA of associated white matter tracts, and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions, but increased in the cerebellum. Conclusions: Increased Aβ burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathological features of dementia, which may relate to axonal damage produced by the injury.
Sapey-Triomphe L-A, Heckemann RA, Boublay N, et al., 2015, Neuroanatomical Correlates of Recognizing Face Expressions in Mild Stages of Alzheimer's Disease, PLOS ONE, Vol: 10, ISSN: 1932-6203
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
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.
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
Merida I, Costes N, Heckemann RA, et al., 2015, EVALUATION OF SEVERAL MULTI-ATLAS METHODS FOR PSEUDO-CT GENERATION IN BRAIN MRI-PET ATTENUATION CORRECTION, IEEE 12th International Symposium on Biomedical Imaging, Publisher: IEEE, Pages: 1431-1434, ISSN: 1945-7928
Ramdeen KT, Heckemann RA, Hammers A, et al., 2014, Amygdalar Atrophy as a Predictor of Cognitive Decline in Mild Cognitive Impairment, Publisher: CANADIAN PSYCHOLOGICAL ASSOC, Pages: 258-258, ISSN: 1196-1961
Barros DAR, McGinnity CJ, Rosso L, et al., 2014, Test-retest reproducibility of cannabinoid-receptor type 1 availability quantified with the PET ligand [C-11]MePPEP, NEUROIMAGE, Vol: 97, Pages: 151-162, ISSN: 1053-8119
Hammers A, Barros RDA, McGinnity CJ, et al., 2014, CANNABINOID RECEPTOR TYPE 1 AVAILABILITY AND SPONTANEOUS TEMPORAL LOBE SEIZURES, 11th European Congress on Epileptology, Publisher: WILEY-BLACKWELL, Pages: 223-223, ISSN: 0013-9580
Rizzo G, Veronese M, Heckemann RA, et al., 2014, The predictive power of brain mRNA mappings for in vivo protein density: a positron emission tomography correlation study, JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 34, Pages: 827-835, ISSN: 0271-678X
Ciumas C, Saignavongs M, Ilski F, et al., 2014, White matter development in children with benign childhood epilepsy with centro-temporal spikes, Joint Congress of European Neurology, Publisher: SPRINGER HEIDELBERG, Pages: S45-S45, ISSN: 0340-5354
Ciumas C, Saignavongs M, Ilski F, et al., 2014, White matter development in children with benign childhood epilepsy with centro-temporal spikes, Joint Congress of European Neurology, Publisher: WILEY-BLACKWELL, Pages: 60-60, ISSN: 1351-5101
Ciumas C, Saignavongs M, Ilski F, et al., 2014, White matter development in children with benign childhood epilepsy with centro-temporal spikes, BRAIN, Vol: 137, Pages: 1095-1106, ISSN: 0006-8950
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
Rubi S, Costes N, Heckemann RA, et al., 2013, Positron emission tomography with alpha-[C-11]methyl-L-tryptophan in tuberous sclerosis complex-related epilepsy, EPILEPSIA, Vol: 54, Pages: 2143-2150, ISSN: 0013-9580
McGinnity CJ, Shidahara M, Feldmann M, et al., 2013, Quantification of opioid receptor availability following spontaneous epileptic seizures: Correction of [C-11]diprenorphine PET data for the partial-volume effect, NEUROIMAGE, Vol: 79, Pages: 72-80, ISSN: 1053-8119
Cross JH, Arora R, Heckemann RA, et al., 2013, Neurological features of epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome, DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, Vol: 55, Pages: 846-856, ISSN: 0012-1622
Butler C, van Erp W, Bhaduri A, et al., 2013, Magnetic resonance volumetry reveals focal brain atrophy in transient epileptic amnesia, EPILEPSY & BEHAVIOR, Vol: 28, Pages: 363-369, ISSN: 1525-5050
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