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{{h2|General Image Processing}} | {{h2|General Image Processing}} | ||
{{h3|GVF Software}} | {{h3|GVF Software}} | ||
| − | * 2D {{static|gvf/|GVF}} code for Matlab is [ | + | * 2D {{static|gvf/|GVF}} code for Matlab is [https://www.nitrc.org/frs/?group_id=271 available by following this link]. |
| − | * 2D Multigrid GVF code in C is [ | + | * 2D Multigrid GVF code in C is [https://www.nitrc.org/frs/?group_id=271 available by following this link]. |
| − | * 3D GVF example Java code is [https://www.nitrc.org/plugins/scmcvs/cvsweb.php/toads-cruise/src/edu/jhu/ece/iacl/algorithms/gvf/GradVecFlowOptimized.java?rev=1.1;content-type=text%2Fx-cvsweb-markup;cvsroot=toads-cruise available here]. This 3D version is built around [ | + | * 3D GVF example Java code is [https://www.nitrc.org/plugins/scmcvs/cvsweb.php/toads-cruise/src/edu/jhu/ece/iacl/algorithms/gvf/GradVecFlowOptimized.java?rev=1.1;content-type=text%2Fx-cvsweb-markup;cvsroot=toads-cruise available here]. This 3D version is built around [https://www.nitrc.org/frs/?group_id=228 JIST]. |
{{h3|JIST}} | {{h3|JIST}} | ||
| − | * The Java Image Science Toolkit (JIST) has a [ | + | * The Java Image Science Toolkit (JIST) has a [https://www.nitrc.org/projects/jist/ project page] and [https://www.nitrc.org/frs/?group_id=228 downloads]. |
{{h3|MGDM}} | {{h3|MGDM}} | ||
| − | * Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the [ | + | * Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the [https://www.nitrc.org/projects/mgdm/ MGDM project page] hosted by NITRC. |
* A [https://iacl.jhu.edu/~john/mgdmMov.avi movie demo] of the decomposition and evolution of MGDM. | * A [https://iacl.jhu.edu/~john/mgdmMov.avi movie demo] of the decomposition and evolution of MGDM. | ||
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{{h3|Progression Score Model}} | {{h3|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 Progression Score Model Toolkit, including the expectation-maximization (EM) algorithm for fitting the nonlinear mixed effects model, can be obtained from [https://www.nitrc.org/projects/progscore/ the NITRC project page]. |
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{{h3|MIMECS}} | {{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}} | * 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 [ | + | * Code and sample data is available on our NITRC Project Page titled [https://www.nitrc.org/projects/image_synthesis/ Image Synthesis]. |
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{{h3|REPLICA}} | {{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}} | * 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 [ | + | * Code and sample data is available on our NITRC Project Page titled [https://www.nitrc.org/projects/image_synthesis/ Image Synthesis]. |
{{h3|Super Resolution}} | {{h3|Super Resolution}} | ||
| − | * [[iSMORE|SMORE]] and [[iSMORE]] are available for download. The methods are described in: | + | * [[iSMORE|SMORE]] and [[iSMORE]] are available for download from [https://gitlab.com/iacl/smore GitLab.com/iacl/smore] |
| + | <!-- | ||
| + | The methods are described in: | ||
::{{pub|author=C. Zhao, M. Shao, A. Carass, H. Li, B.E. Dewey, L.M. Ellingsen, J. Woo, M.A. Guttman, A.M. Blitz, M. Stone, P.A. Calabresi, H. Halperin, and J.L. Prince|title=Applications of a deep learning method for anti-aliasing and super-resolution in MRI|jrnl=mrm|number=64:132-141|when=2019|doi=10.1016/j.mri.2019.05.038|pubmed=31247254}} | ::{{pub|author=C. Zhao, M. Shao, A. Carass, H. Li, B.E. Dewey, L.M. Ellingsen, J. Woo, M.A. Guttman, A.M. Blitz, M. Stone, P.A. Calabresi, H. Halperin, and J.L. Prince|title=Applications of a deep learning method for anti-aliasing and super-resolution in MRI|jrnl=mrm|number=64:132-141|when=2019|doi=10.1016/j.mri.2019.05.038|pubmed=31247254}} | ||
::{{pub|author=C. Zhao, S. Son, Y. Kim, and J.L. Prince|title=iSMORE: An Iterative Self Super-Resolution Algorithm|when=Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the {{iacl-pub miccai2019}}|number=130-139|period=|doi=10.1007/978-3-030-32778-1_14}} | ::{{pub|author=C. Zhao, S. Son, Y. Kim, and J.L. Prince|title=iSMORE: An Iterative Self Super-Resolution Algorithm|when=Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the {{iacl-pub miccai2019}}|number=130-139|period=|doi=10.1007/978-3-030-32778-1_14}} | ||
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* [[CALAMITI|Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI)]] is an MR Harmonization framework described in: | * [[CALAMITI|Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI)]] is an MR Harmonization framework described in: | ||
::{{pub| author = L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince| title = Information-based Disentangled Representation Learning for Unsupervised MR Harmonization| conf = ipmi2021}} | ::{{pub| author = L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince| title = Information-based Disentangled Representation Learning for Unsupervised MR Harmonization| conf = ipmi2021}} | ||
| + | ::{{pub| author= L. Zuo, B. E. Dewey, Y. Liu, Y. He, S. D. Newsome, E. M. Mowry, S. M. Resnick, J. L. Prince, and A. Carass| title = Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory| jrnl=ni | number= 118569 | when=2021| doi=10.1016/j.neuroimage.2021.118569}} | ||
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| + | {{h3|HACA3}} | ||
| + | * [https://github.com/lianruizuo/haca3 HACA3 Code] for MR Harmonization described in: | ||
| + | :: {{pub| author = L. Zuo, Y. Liu, Y. Xue, B.E. Dewey, S.W. Remedios, S.P. Hays, M. Bilgel, E.M. Mowry, S.D. Newsome, P.A. Calabresi, S.M. Resnick, J.L. Prince, and A. Carass | title = HACA3: A unified approach for multi-site MR image harmonization | jrnl = cmig | number = 109:102285 | when = 2023 | doi = 10.1016/j.compmedimag.2023.102285 | arXiv = 2212.06065}} | ||
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{{h2|Retinal}} | {{h2|Retinal}} | ||
{{h3|AURA Tools}} | {{h3|AURA Tools}} | ||
| − | * The [ | + | * The [https://www.nitrc.org/projects/aura_tools/ 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: {{pub|author=A. Lang, A. Carass, M. Hauser, E.S. Sotirchos, P.A. Calabresi, H.S. Ying, and J.L. Prince|title=[[Retinal layer segmentation of macular OCT images|Retinal layer segmentation of macular OCT images using boundary classification]]|jrnl=bmoe|number=4(7):1133-1152|when=2013|doi=10.1364/BOE.4.001133|pmcid=3704094|pdf=/proceedings/iacl/2013/LanxBOE13-Retinal_layer_OCT.pdf}} | :* Version 1.2 includes the software originally presented in: {{pub|author=A. Lang, A. Carass, M. Hauser, E.S. Sotirchos, P.A. Calabresi, H.S. Ying, and J.L. Prince|title=[[Retinal layer segmentation of macular OCT images|Retinal layer segmentation of macular OCT images using boundary classification]]|jrnl=bmoe|number=4(7):1133-1152|when=2013|doi=10.1364/BOE.4.001133|pmcid=3704094|pdf=/proceedings/iacl/2013/LanxBOE13-Retinal_layer_OCT.pdf}} | ||
{{h3|ASiMOV}} | {{h3|ASiMOV}} | ||
| − | * The Automated segmentation of mouse OCT volumes (ASiMOV) toolkit is now available from the [ | + | * The Automated segmentation of mouse OCT volumes (ASiMOV) toolkit is now available from the [https://www.nitrc.org/projects/aura_tools/ AURA Tools NITRC page]. |
:* Version 0.1 includes the software originally presented in: {{pub|author=B.J. Antony, B.-J. Kim, A. Lang, A. Carass, J.L. Prince, and D.J. Zack|title=Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model|journal=PLoS ONE|number=12(8):e0181059|when=2017|doi=10.1371/journal.pone.0181059|pubmed=28817571}} | :* Version 0.1 includes the software originally presented in: {{pub|author=B.J. Antony, B.-J. Kim, A. Lang, A. Carass, J.L. Prince, and D.J. Zack|title=Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model|journal=PLoS ONE|number=12(8):e0181059|when=2017|doi=10.1371/journal.pone.0181059|pubmed=28817571}} | ||
:* Direct download link for [https://www.nitrc.org/frs/download.php/10836/ASiMOV-0.1.zip ASiMOV v0.1] | :* Direct download link for [https://www.nitrc.org/frs/download.php/10836/ASiMOV-0.1.zip ASiMOV v0.1] | ||
| + | |||
| + | {{h3|VICCE}} | ||
| + | * [[VICCE|Variational Intensity Cross Channel Encoder (VICCE)]] is a vessel segmentation algorithm for 2D OCTA images described in: | ||
| + | ::{{pub|author = Y. Liu, L. Zuo, A. Carass, Y. He, A. Filippatou, S.D. Solomon, S. Saidha, P.A. Calabresi, and J.L. Prince | title = [https://iacl.jhu.edu/~yihao/papers/SPIE-2020-VICCE.pdf Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography] | conf = spie2020 | doi = 10.1117/12.2549967| pdf = /~yihao/papers/SPIE-2020-VICCE.pdf}} | ||
| + | |||
| + | {{h3|ACRROSS}} | ||
| + | * [[ACRROSS|Artifacts and Contrast Robust Representation for OCTA Semi-supervised Segmentation (ACRROSS)]] is a vessel segmentation algorithm for en face OCTA images. | ||
{{:OCT Data}} | {{:OCT Data}} | ||
Latest revision as of 02:56, 12 October 2023
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 patients. Results can still be submitted for testing through the 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.
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.
Cerebellum
GraphCut Parcellation
- Our graph-cut based parcellation 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.
ACAPULCO
- ACAPULCO, our premier cerebellum parcellation software, is available for download. The method is described in:
- S. Han, A. Carass, Y. He, and J.L. Prince, "Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization", NeuroImage, 218:116819, 2020. (doi)
- Both Adult and Pediatric versions of ACAPULCO are available for download.
IMAGE Synthesis and Harmonization
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.
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.
Super Resolution
- SMORE and iSMORE are available for download from GitLab.com/iacl/smore
CALAMITI
- Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI) is an MR Harmonization framework described in:
- L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince, "Information-based Disentangled Representation Learning for Unsupervised MR Harmonization", 27th Conference on Information Processing in Medical Imaging (IPMI 2021), Virtually in Bornholm, Denmark, June 28 - July 2, 2021.
- L. Zuo, B. E. Dewey, Y. Liu, Y. He, S. D. Newsome, E. M. Mowry, S. M. Resnick, J. L. Prince, and A. Carass, "Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory", NeuroImage, 118569, 2021. (doi)
HACA3
- HACA3 Code for MR Harmonization described in:
- L. Zuo, Y. Liu, Y. Xue, B.E. Dewey, S.W. Remedios, S.P. Hays, M. Bilgel, E.M. Mowry, S.D. Newsome, P.A. Calabresi, S.M. Resnick, J.L. Prince, and A. Carass, "HACA3: A unified approach for multi-site MR image harmonization", Computerized Medical Imaging and Graphics, 109:102285, 2023. (doi)
- L. Zuo, Y. Liu, Y. Xue, B.E. Dewey, S.W. Remedios, S.P. Hays, M. Bilgel, E.M. Mowry, S.D. Newsome, P.A. Calabresi, S.M. Resnick, J.L. Prince, and A. Carass, "HACA3: A unified approach for multi-site MR image harmonization", Computerized Medical Imaging and Graphics, 109:102285, 2023. (doi)
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)
ASiMOV
- The Automated segmentation of mouse OCT volumes (ASiMOV) toolkit is now available from the AURA Tools NITRC page.
- Version 0.1 includes the software originally presented in: B.J. Antony, B.-J. Kim, A. Lang, A. Carass, J.L. Prince, and D.J. Zack, "Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model", PLoS ONE, 12(8):e0181059, 2017. (doi) (PubMed)
- Direct download link for ASiMOV v0.1
VICCE
- Variational Intensity Cross Channel Encoder (VICCE) is a vessel segmentation algorithm for 2D OCTA images described in:
- Y. Liu, L. Zuo, A. Carass, Y. He, A. Filippatou, S.D. Solomon, S. Saidha, P.A. Calabresi, and J.L. Prince, "Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography", Proceedings of SPIE Medical Imaging (SPIE-MI 2020), Houston, TX, February 15 - 20, 2020. (PDF) (doi)
ACRROSS
- Artifacts and Contrast Robust Representation for OCTA Semi-supervised Segmentation (ACRROSS) is a vessel segmentation algorithm for en face OCTA images.
OCT MS and Healthy Controls Data
- Data resource for Multiple Sclerosis and Healthy Controls:
- OCT_Manual_Delineations-2018_June_29.zip (1.8G) contains 35 OCT volumes from a Spectralis Scanner with corresponding manual delineations of nine retinal boundaries. The cohort contains 14 healthy controls and 21 multiple sclerosis patients with age and gender information.
- The preprocessing and data reader code are in a GitHub Repository.
- If you use this data please cite: Y. He, A. Carass, S.D. Solomon, S. Saidha, P.A. Calabresi, and J.L. Prince, "Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls", Data in Brief, 22:601-604, 2019. (doi) (PubMed) (PMCID 6327073)
IACL Mouse Data
- In conjunction with the paper: B.J. Antony, B.-J. Kim, A. Lang, A. Carass, J.L. Prince, and D.J. Zack, "Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model", PLoS ONE, 12(8):e0181059, 2017. (doi) (PubMed) We release mice_data_2017-July-18.zip which is the data and results presented in this paper. When using this data please cite the PLOS ONE paper. The download is 15.2GB.
