Difference between revisions of "Resources"
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* Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the [http://www.nitrc.org/projects/mgdm/ MGDM project page] hosted by NITRC. | * Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the [http://www.nitrc.org/projects/mgdm/ MGDM project page] hosted by NITRC. | ||
| − | * A [ | + | * A [https://iacl.jhu.edu/~john/mgdmMov.avi movie demo] of the decomposition and evolution of MGDM. |
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* The temporal filtering of longitudinal MR images of the brain described in {{pub|author=S. Roy, A. Carass, J. Pacheco, M. Bilgel, S.M. Resnick, J.L. Prince, and D.L. Pham|title=Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation|journal=NeuroImage: Clinical|number=11:264-275|when=2016|doi=doi:10.1016/j.nicl.2016.02.005}} | * The temporal filtering of longitudinal MR images of the brain described in {{pub|author=S. Roy, A. Carass, J. Pacheco, M. Bilgel, S.M. Resnick, J.L. Prince, and D.L. Pham|title=Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation|journal=NeuroImage: Clinical|number=11:264-275|when=2016|doi=doi:10.1016/j.nicl.2016.02.005}} | ||
* Matlab executables are {{iacl|~roy/softwares/4dfilter_Feb19-2016.zip|available}} | * Matlab executables are {{iacl|~roy/softwares/4dfilter_Feb19-2016.zip|available}} | ||
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* The PET attentuation correction method described in {{pub|author=S. Roy, W.-T. Wang, A. Carass, J.L. Prince, J.A. Butman, and D.L. Pham|title=[[PET Attenuation Correction using Synthetic CT from Ultrashort Echo-time MRI|PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging]]|journal=Jrnl. of Nuclear Medicine|number=55:1-7|when=2014|doi=10.2967/jnumed.114.143958}} | * The PET attentuation correction method described in {{pub|author=S. Roy, W.-T. Wang, A. Carass, J.L. Prince, J.A. Butman, and D.L. Pham|title=[[PET Attenuation Correction using Synthetic CT from Ultrashort Echo-time MRI|PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging]]|journal=Jrnl. of Nuclear Medicine|number=55:1-7|when=2014|doi=10.2967/jnumed.114.143958}} | ||
* Matlab executables are {{iacl|~roy/softwares/CTsynthesis.zip|available}}. | * Matlab executables are {{iacl|~roy/softwares/CTsynthesis.zip|available}}. | ||
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* The Progression Score Model Toolkit, including the expectation-maximization (EM) algorithm for fitting the nonlinear mixed effects model, can be obtained from [http://www.nitrc.org/projects/progscore/ the NITRC project page]. | * The Progression Score Model Toolkit, including the expectation-maximization (EM) algorithm for fitting the nonlinear mixed effects model, can be obtained from [http://www.nitrc.org/projects/progscore/ the NITRC project page]. | ||
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| + | {{h2|IMAGE Synthesis}} | ||
| + | {{h3|MIMECS}} | ||
| + | * MIMECS is described in: {{pub|author=S. Roy, A. Carass, and J.L. Prince|title=[[Magnetic Resonance Image Example Based Contrast Synthesis|Magnetic Resonance Image Example Based Contrast Synthesis]]|jrnl=tmi|number=32(12):2348-2363|when=2013|doi=10.1109/TMI.2013.2282126|pdf=/proceedings/iacl/2013/RoyxTMI13-MIMECS.pdf}} | ||
| + | * Code and sample data is available on our NITRC Project Page titled [http://www.nitrc.org/projects/image_synthesis/ Image Synthesis]. | ||
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| + | {{h3|Subject Specific Dictionary Learning (S3DL)}} | ||
| + | * S3DL described in {{pub|author=S. Roy, A. Carass, J.L. Prince, and D.L. Pham|title=Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation|conf=mlmi2014|doi=10.1007/978-3-319-10581-9_31|period=}} and {{pub|author=S. Roy, Q. He, E. Sweeney, A. Carass, D.S. Reich, J.L. Prince, and D.L. Pham|title=Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation|journal=IEEE Journal of Biomedical and Health Informatics|number=19(5):1598-1609|when=2015|doi=10.1109/JBHI.2015.2439242}} | ||
| + | * Matlab executables are {{iacl|~roy/softwares/S3DL_withlesion.zip|available}}. | ||
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| + | {{h3|REPLICA}} | ||
| + | * Regression Ensembles with Patch Learning for Image Contrast Agreement (REPLICA) described in {{pub|author=A. Jog, A. Carass, S. Roy, D.L. Pham, and J.L. Prince|title=Random Forest Regression for Magnetic Resonance Image Synthesis|jrnl=mia|number=35:475-488|when=2017|doi=10.1016/j.media.2016.08.009|pubmed=27607469}} | ||
| + | * Code and sample data is available on our NITRC Project Page titled [http://www.nitrc.org/projects/image_synthesis/ Image Synthesis]. | ||
Revision as of 19:59, 25 September 2017
<meta name="title" content="Resources"/>
General Image Processing
GVF Software
- 2D GVF code for Matlab is available by following this link.
- 2D Multigrid GVF code in C is available by following this link.
- 3D GVF example Java code is available here. This 3D version is built around JIST.
JIST
- The Java Image Science Toolkit (JIST) has a project page and downloads.
MGDM
- Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the MGDM project page hosted by NITRC.
- A movie demo of the decomposition and evolution of MGDM.
Brain
2015 Longitudinal MS Lesion Segmentation Challenge
- The 2015 Longitudinal MS Lesion Segmentation Challenge provides training and testing data for segmenting MS lesions over a multiple time-points of 14 patietns. Results can be tested through an automated website.
Validation Data for Cortical Reconstruction Algorithms
- The cortical validation resource for evaluation of cortical reconstruction algorithms on both normal subjects and subjects with White Matter lesions.
Cerebellar Lobule Segmentation using Graph Cuts
- The graph-cut based segmentation of the cerebellum described in Z. Yang, C. Ye, J.A. Bogovic, A. Carass, B.M. Jedynak, S.H. Ying, and J.L. Prince, "Automated Cerebellar Lobule Segmentation with Application to Cerebellar Structural Analysis in Cerebellar Disease", NeuroImage, 127:435-444, 2016. (doi)
- Cerebellar Lobule Segmentation Code can be used to parcellate the cerebellum into lobules given a T1w MRI image.
Temporal Filtering for Consistent Segmentation
- The temporal filtering of longitudinal MR images of the brain described in S. Roy, A. Carass, J. Pacheco, M. Bilgel, S.M. Resnick, J.L. Prince, and D.L. Pham, "Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation", NeuroImage: Clinical, 11:264-275, 2016. (doi)
- Matlab executables are available
PET Attentuation Correction
- The PET attentuation correction method described in S. Roy, W.-T. Wang, A. Carass, J.L. Prince, J.A. Butman, and D.L. Pham, "PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging", Jrnl. of Nuclear Medicine, 55:1-7, 2014. (doi)
- Matlab executables are available.
Rician Mixture Model
- The Rician mixture model for segmenting the brain is described in S. Roy, A. Carass, P.-L. Bazin, S.M. Resnick, and J.L. Prince, "Consistent Segmentation using a Rician Classifier", Medical Image Analysis, 16(2):524-535, 2012. (PDF) (doi) (PubMed)
- Matlab executables are available.
Progression Score Model
- The Progression Score Model Toolkit, including the expectation-maximization (EM) algorithm for fitting the nonlinear mixed effects model, can be obtained from the NITRC project page.
IMAGE Synthesis
MIMECS
- MIMECS is described in: S. Roy, A. Carass, and J.L. Prince, "Magnetic Resonance Image Example Based Contrast Synthesis", IEEE Trans. on Medical Imaging, 32(12):2348-2363, 2013. (PDF) (doi)
- Code and sample data is available on our NITRC Project Page titled Image Synthesis.
Subject Specific Dictionary Learning (S3DL)
- S3DL described in S. Roy, A. Carass, J.L. Prince, and D.L. Pham, "Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation", Fifth International Workshop on Machine Learning in Medical Imaging (MLMI 2014), Boston, MA, September 14, 2014. (doi) and S. Roy, Q. He, E. Sweeney, A. Carass, D.S. Reich, J.L. Prince, and D.L. Pham, "Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation", IEEE Journal of Biomedical and Health Informatics, 19(5):1598-1609, 2015. (doi)
- Matlab executables are available.
REPLICA
- Regression Ensembles with Patch Learning for Image Contrast Agreement (REPLICA) described in A. Jog, A. Carass, S. Roy, D.L. Pham, and J.L. Prince, "Random Forest Regression for Magnetic Resonance Image Synthesis", Medical Image Analysis, 35:475-488, 2017. (doi) (PubMed)
- Code and sample data is available on our NITRC Project Page titled Image Synthesis.
Cardiac
HARP
- For MATLAB demonstration software send an email to
and expect a reply within five business days. We also have a collection of frequently asked questions about our HARP software.
Retinal
AURA Tools
- The AURA Tools software package allows for the automated processing and segmentation of Optical Coherence Tomography images of the macula cube. It is available from the NITRC website.
- Version 1.2 includes the software originally presented in: A. Lang, A. Carass, M. Hauser, E.S. Sotirchos, P.A. Calabresi, H.S. Ying, and J.L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification", Biomedical Optics Express, 4(7):1133-1152, 2013. (PDF) (doi) (PMCID 3704094)
