Search Results - "Vladimir Fonov"

SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data by De Leener, Benjamin, Lévy, Simon, Dupont, Sara M., Fonov, Vladimir S., Stikov, Nikola, Louis Collins, D., Callot, Virginie, Cohen-Adad, Julien

“…For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance…”
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A comparison of publicly available linear MRI stereotaxic registration techniques by Dadar, Mahsa, Fonov, Vladimir S., Collins, D. Louis

“…Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an…”
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PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space by De Leener, Benjamin, Fonov, Vladimir S., Collins, D. Louis, Callot, Virginie, Stikov, Nikola, Cohen-Adad, Julien

“…Template-based analysis of multi-parametric MRI data of the spinal cord sets the foundation for standardization and reproducibility, thereby helping the…”
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Structural imaging biomarkers of Alzheimer's disease: predicting disease progression by Eskildsen, Simon F, Coupé, Pierrick, Fonov, Vladimir S, Pruessner, Jens C, Collins, D. Louis

“…Abstract Optimized magnetic resonance imaging (MRI)–based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the…”
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Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge by Wang, Li, Nie, Dong, Li, Guannan, Puybareau, Elodie, Dolz, Jose, Zhang, Qian, Wang, Fan, Xia, Jing, Wu, Zhengwang, Chen, Jia-Wei, Thung, Kim-Han, Bui, Toan Duc, Shin, Jitae, Zeng, Guodong, Zheng, Guoyan, Fonov, Vladimir S., Doyle, Andrew, Xu, Yongchao, Moeskops, Pim, Pluim, Josien P. W., Desrosiers, Christian, Ayed, Ismail Ben, Sanroma, Gerard, Benkarim, Oualid M., Casamitjana, Adria, Vilaplana, Veronica, Lin, Weili, Li, Gang, Shen, Dinggang

“…Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid is an indispensable…”
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Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease by Coupé, Pierrick, Eskildsen, Simon F., Manjón, José V., Fonov, Vladimir S., Collins, D. Louis

“…In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic…”
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Bloody noise: The impact of blood‐flow artifacts on registration by Lewis, John D., Fonov, Vladimir S., Collins, D. Louis

“…Blood‐flow artifacts present a serious challenge for most, if not all, volumetric analytical approaches. We utilize T1‐weighted data with prominent blood‐flow…”
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Identifying incipient dementia individuals using machine learning and amyloid imaging by Mathotaarachchi, Sulantha, Pascoal, Tharick A., Shin, Monica, Benedet, Andrea L., Kang, Min Su, Beaudry, Thomas, Fonov, Vladimir S., Gauthier, Serge, Rosa-Neto, Pedro

“…Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design…”
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The Canadian Dementia Imaging Protocol: Harmonizing National Cohorts by Duchesne, Simon, Chouinard, Isabelle, Potvin, Olivier, Fonov, Vladimir S., Khademi, April, Bartha, Robert, Bellec, Pierre, Collins, D. Louis, Descoteaux, Maxime, Hoge, Rick, McCreary, Cheryl R., Ramirez, Joel, Scott, Christopher J.M., Smith, Eric E., Strother, Stephen C., Black, Sandra E.

“…Background Harmonized protocols to collect imaging data must be devised, employed, and maintained in multicentric studies to reduce interscanner variability in…”
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Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging by Dadar, Mahsa, Maranzano, Josefina, Misquitta, Karen, Anor, Cassandra J., Fonov, Vladimir S., Tartaglia, M. Carmela, Carmichael, Owen T., Decarli, Charles, Collins, D. Louis

“…White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological…”
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Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation by Coupé, Pierrick, Manjón, José V., Fonov, Vladimir, Pruessner, Jens, Robles, Montserrat, Collins, D. Louis

“…Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently,…”
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Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images by Carass, Aaron, Cuzzocreo, Jennifer L., Han, Shuo, Hernandez-Castillo, Carlos R., Rasser, Paul E., Ganz, Melanie, Beliveau, Vincent, Dolz, Jose, Ben Ayed, Ismail, Desrosiers, Christian, Thyreau, Benjamin, Romero, José E., Coupé, Pierrick, Manjón, José V., Fonov, Vladimir S., Collins, D. Louis, Ying, Sarah H., Onyike, Chiadi U., Crocetti, Deana, Landman, Bennett A., Mostofsky, Stewart H., Thompson, Paul M., Prince, Jerry L.

“…The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to…”
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Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change by Bezgin, Gleb, Lewis, John D, Fonov, Vladimir S, Collins, D Louis, Evans, Alan C

“…Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically…”
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CERES: A new cerebellum lobule segmentation method by Romero, Jose E., Coupé, Pierrick, Giraud, Rémi, Ta, Vinh-Thong, Fonov, Vladimir, Park, Min Tae M., Chakravarty, M. Mallar, Voineskos, Aristotle N., Manjón, Jose V.

“…The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and…”
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BEaST: Brain extraction based on nonlocal segmentation technique by Eskildsen, Simon F., Coupé, Pierrick, Fonov, Vladimir, Manjón, José V., Leung, Kelvin K., Guizard, Nicolas, Wassef, Shafik N., Østergaard, Lasse Riis, Collins, D. Louis

“…Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due…”
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Unbiased age-specific structural brain atlases for Chinese pediatric population by Zhao, Tengda, Liao, Xuhong, Fonov, Vladimir S., Wang, Qiushi, Men, Weiwei, Wang, Yanpei, Qin, Shaozheng, Tan, Shuping, Gao, Jia-Hong, Evans, Alan, Tao, Sha, Dong, Qi, He, Yong

“…In magnetic resonance (MR) imaging studies of child brain development, structural brain atlases usually serve as important references for the pediatric…”
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DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template by Fonov, Vladimir S., Dadar, Mahsa, ADNI, The PREVENT-AD Research Group, Collins, D. Louis

“…Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is…”
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Cortical and subcortical T1 white/gray contrast, chronological age, and cognitive performance by Lewis, John D., Fonov, Vladimir S., Collins, D. Louis, Evans, Alan C., Tohka, Jussi

“…The maturational schedule of typical brain development is tightly constrained; deviations from it are associated with cognitive atypicalities, and are…”
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A sub+cortical fMRI‐based surface parcellation by Lewis, John D., Bezgin, Gleb, Fonov, Vladimir S., Collins, D. Louis, Evans, Alan C.

“…Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping…”
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Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease by Dadar, Mahsa, Pascoal, Tharick A., Manitsirikul, Sarinporn, Misquitta, Karen, Fonov, Vladimir S., Tartaglia, M. Carmela, Breitner, John, Rosa-Neto, Pedro, Carmichael, Owen T., Decarli, Charles, Collins, D. Louis

“…Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging…”
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