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MINC 2.0: A Flexible Format for Multi-Modal Images.

Frontiers in neuroinformatics

Vincent RD, Neelin P, Khalili-Mahani N, Janke AL, Fonov VS, Robbins SM, Baghdadi L, Lerch J, Sled JG, Adalat R, MacDonald D, Zijdenbos AP, Collins DL, Evans AC.
PMID: 27563289
Front Neuroinform. 2016 Aug 11;10:35. doi: 10.3389/fninf.2016.00035. eCollection 2016.

It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as...

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Vincent RD, Neelin P, Khalili-Mahani N, et al. MINC 2.0: A Flexible Format for Multi-Modal Images. Front Neuroinform. 2016;10:35doi: 10.3389/fninf.2016.00035.
Vincent, R. D., Neelin, P., Khalili-Mahani, N., Janke, A. L., Fonov, V. S., Robbins, S. M., Baghdadi, L., Lerch, J., Sled, J. G., Adalat, R., MacDonald, D., Zijdenbos, A. P., Collins, D. L., & Evans, A. C. (2016). MINC 2.0: A Flexible Format for Multi-Modal Images. Frontiers in neuroinformatics, 1035. https://doi.org/10.3389/fninf.2016.00035
Vincent, Robert D, et al. "MINC 2.0: A Flexible Format for Multi-Modal Images." Frontiers in neuroinformatics vol. 10 (2016): 35. doi: https://doi.org/10.3389/fninf.2016.00035
Vincent RD, Neelin P, Khalili-Mahani N, Janke AL, Fonov VS, Robbins SM, Baghdadi L, Lerch J, Sled JG, Adalat R, MacDonald D, Zijdenbos AP, Collins DL, Evans AC. MINC 2.0: A Flexible Format for Multi-Modal Images. Front Neuroinform. 2016 Aug 11;10:35. doi: 10.3389/fninf.2016.00035. eCollection 2016. PMID: 27563289; PMCID: PMC4980430.
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A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines.

Frontiers in neuroinformatics

OmidYeganeh M, Khalili-Mahani N, Bermudez P, Ross A, Lepage C, Vincent RD, Jeon S, Lewis LB, Das S, Zijdenbos AP, Rioux P, Adalat R, Van Eede MC, Evans AC.
PMID: 34381348
Front Neuroinform. 2021 Jul 26;15:665560. doi: 10.3389/fninf.2021.665560. eCollection 2021.

In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem,...

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OmidYeganeh M, Khalili-Mahani N, Bermudez P, et al. A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines. Front Neuroinform. 2021;15:665560doi: 10.3389/fninf.2021.665560.
OmidYeganeh, M., Khalili-Mahani, N., Bermudez, P., Ross, A., Lepage, C., Vincent, R. D., Jeon, S., Lewis, L. B., Das, S., Zijdenbos, A. P., Rioux, P., Adalat, R., Van Eede, M. C., & Evans, A. C. (2021). A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines. Frontiers in neuroinformatics, 15665560. https://doi.org/10.3389/fninf.2021.665560
OmidYeganeh, Mona, et al. "A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines." Frontiers in neuroinformatics vol. 15 (2021): 665560. doi: https://doi.org/10.3389/fninf.2021.665560
OmidYeganeh M, Khalili-Mahani N, Bermudez P, Ross A, Lepage C, Vincent RD, Jeon S, Lewis LB, Das S, Zijdenbos AP, Rioux P, Adalat R, Van Eede MC, Evans AC. A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines. Front Neuroinform. 2021 Jul 26;15:665560. doi: 10.3389/fninf.2021.665560. eCollection 2021. PMID: 34381348; PMCID: PMC8350777.
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Correction of intensity variations in MR images for computer-aided tissue classification.

IEEE transactions on medical imaging

Dawant BM, Zijdenbos AP, Margolin RA.
PMID: 18218473
IEEE Trans Med Imaging. 1993;12(4):770-81. doi: 10.1109/42.251128.

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice,...

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Dawant BM, Zijdenbos AP, Margolin RA. Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Trans Med Imaging. 1993;12(4):770-81doi: 10.1109/42.251128.
Dawant, B. M., Zijdenbos, A. P., & Margolin, R. A. (1993). Correction of intensity variations in MR images for computer-aided tissue classification. IEEE transactions on medical imaging, 12(4), 770-81. https://doi.org/10.1109/42.251128
Dawant, B M, et al. "Correction of intensity variations in MR images for computer-aided tissue classification." IEEE transactions on medical imaging vol. 12,4 (1993): 770-81. doi: https://doi.org/10.1109/42.251128
Dawant BM, Zijdenbos AP, Margolin RA. Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Trans Med Imaging. 1993;12(4):770-81. doi: 10.1109/42.251128. PMID: 18218473.
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LORIS: a web-based data management system for multi-center studies.

Frontiers in neuroinformatics

Das S, Zijdenbos AP, Harlap J, Vins D, Evans AC.
PMID: 22319489
Front Neuroinform. 2012 Jan 20;5:37. doi: 10.3389/fninf.2011.00037. eCollection 2011.

Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination....

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Das S, Zijdenbos AP, Harlap J, et al. LORIS: a web-based data management system for multi-center studies. Front Neuroinform. 2012;5:37doi: 10.3389/fninf.2011.00037.
Das, S., Zijdenbos, A. P., Harlap, J., Vins, D., & Evans, A. C. (2011). LORIS: a web-based data management system for multi-center studies. Frontiers in neuroinformatics, 537. https://doi.org/10.3389/fninf.2011.00037
Das, Samir, et al. "LORIS: a web-based data management system for multi-center studies." Frontiers in neuroinformatics vol. 5 (2011): 37. doi: https://doi.org/10.3389/fninf.2011.00037
Das S, Zijdenbos AP, Harlap J, Vins D, Evans AC. LORIS: a web-based data management system for multi-center studies. Front Neuroinform. 2012 Jan 20;5:37. doi: 10.3389/fninf.2011.00037. eCollection 2011. PMID: 22319489; PMCID: PMC3262165.
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The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows.

Frontiers in neuroinformatics

Bellec P, Lavoie-Courchesne S, Dickinson P, Lerch JP, Zijdenbos AP, Evans AC.
PMID: 22493575
Front Neuroinform. 2012 Apr 03;6:7. doi: 10.3389/fninf.2012.00007. eCollection 2012.

The analysis of neuroimaging databases typically involves a large number of inter-connected steps called a pipeline. The pipeline system for Octave and Matlab (PSOM) is a flexible framework for the implementation of pipelines in the form of Octave or...

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Bellec P, Lavoie-Courchesne S, Dickinson P, et al. The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Front Neuroinform. 2012;6:7doi: 10.3389/fninf.2012.00007.
Bellec, P., Lavoie-Courchesne, S., Dickinson, P., Lerch, J. P., Zijdenbos, A. P., & Evans, A. C. (2012). The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Frontiers in neuroinformatics, 67. https://doi.org/10.3389/fninf.2012.00007
Bellec, Pierre, et al. "The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows." Frontiers in neuroinformatics vol. 6 (2012): 7. doi: https://doi.org/10.3389/fninf.2012.00007
Bellec P, Lavoie-Courchesne S, Dickinson P, Lerch JP, Zijdenbos AP, Evans AC. The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Front Neuroinform. 2012 Apr 03;6:7. doi: 10.3389/fninf.2012.00007. eCollection 2012. PMID: 22493575; PMCID: PMC3318188.
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Morphometric analysis of white matter lesions in MR images: method and validation.

IEEE transactions on medical imaging

Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC.
PMID: 18218550
IEEE Trans Med Imaging. 1994;13(4):716-24. doi: 10.1109/42.363096.

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present...

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Zijdenbos AP, Dawant BM, Margolin RA, et al. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging. 1994;13(4):716-24doi: 10.1109/42.363096.
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994). Morphometric analysis of white matter lesions in MR images: method and validation. IEEE transactions on medical imaging, 13(4), 716-24. https://doi.org/10.1109/42.363096
Zijdenbos, A P, et al. "Morphometric analysis of white matter lesions in MR images: method and validation." IEEE transactions on medical imaging vol. 13,4 (1994): 716-24. doi: https://doi.org/10.1109/42.363096
Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging. 1994;13(4):716-24. doi: 10.1109/42.363096. PMID: 18218550.
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Showing 1 to 6 of 6 entries