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{{h2|Cerebellum}}
 
{{h2|Cerebellum}}
{{h3|GraphCut Cerebellar Lobule Parcellation}}
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{{h3|GraphCut Parcellation}}
* Our graph-cut based segmentation of the cerebellum described in {{pub|author=Z. Yang, C. Ye, J.A. Bogovic, A. Carass, B.M. Jedynak, S.H. Ying, and J.L. Prince|title=Automated Cerebellar Lobule Segmentation with Application to Cerebellar Structural Analysis in Cerebellar Disease|jrnl=ni|number=127:435-444|when=2016|doi=10.1016/j.neuroimage.2015.09.032}}
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* Our graph-cut based parcellation of the cerebellum described in {{pub|author=Z. Yang, C. Ye, J.A. Bogovic, A. Carass, B.M. Jedynak, S.H. Ying, and J.L. Prince|title=Automated Cerebellar Lobule Segmentation with Application to Cerebellar Structural Analysis in Cerebellar Disease|jrnl=ni|number=127:435-444|when=2016|doi=10.1016/j.neuroimage.2015.09.032}}
 
:* {{iacl|~amod/cerlobule_seg_release_06_01_2016.tar.gz|Cerebellar Lobule Segmentation Code}} can be used to parcellate the cerebellum into lobules given a T1w MRI image.
 
:* {{iacl|~amod/cerlobule_seg_release_06_01_2016.tar.gz|Cerebellar Lobule Segmentation Code}} can be used to parcellate the cerebellum into lobules given a T1w MRI image.
  
  
{{h3|ACAPULCO Cerebellar Lobule Parcellation}}
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{{h3|ACAPULCO}}
* A {{iacl|~shuo/data/cerebellum-parcellation_v2.simg|Singularity image}} for '''ACAPULCO''' ({{pub| author=S. Han, A. Carass, Y. He, and J.L. Prince| title=Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization| jrnl=ni| number=218:116819| when=2020| doi=10.1016/j.neuroimage.2020.116819}}). The download is 1.3GB. [[Cerebellum CNN|Instructions]].
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* '''[[Cerebellum CNN|ACAPULCO]]''', our premier cerebellum parcellation software, is available for download. The method is described in:
 +
::{{pub| author=S. Han, A. Carass, Y. He, and J.L. Prince| title=Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization| jrnl=ni| number=218:116819| when=2020| doi=10.1016/j.neuroimage.2020.116819}}
 +
:Both Adult and Pediatric versions of ACAPULCO are available for download.
  
  
  
{{h2|IMAGE Synthesis}}
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{{h2|IMAGE Synthesis and Harmonization}}
 
{{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}}
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{{h3|Subject Specific Dictionary Learning (S3DL)}}
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{{h3|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}}
 
* 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}}.
 
* Matlab executables are {{iacl|~roy/softwares/S3DL_withlesion.zip|available}}.
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* 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 [http://www.nitrc.org/projects/image_synthesis/ Image Synthesis].
 
* Code and sample data is available on our NITRC Project Page titled [http://www.nitrc.org/projects/image_synthesis/ Image Synthesis].
 +
 +
 +
{{h3|Super Resolution}}
 +
* [[iSMORE|SMORE]] and [[iSMORE]] are available for download. 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, 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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{{h3|CALAMITI}}
 +
* [[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, 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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:* 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 = [http://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}}
  
  

Latest revision as of 10:36, 9 September 2021

General Image Processing

GVF Software


JIST


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


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


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)


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


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. The methods are described in:
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, "Applications of a deep learning method for anti-aliasing and super-resolution in MRI", Magnetic Resonance in Medicine, 64:132-141, 2019. (doi) (PubMed)
C. Zhao, S. Son, Y. Kim, and J.L. Prince, "iSMORE: An Iterative Self Super-Resolution Algorithm", 130-139, Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 13 - 17, 2019. (doi)


CALAMITI

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)


Cardiac

HARP

  • For MATLAB demonstration software send an email to Harp email.jpg 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.


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

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)



OCT MS and Healthy Controls Data

  • Data resource for Multiple Sclerosis and Healthy Controls:


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.