Difference between revisions of "Shuo"

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[[File:2019-MICCAI-Shuo-01.jpg|200px|thumb|right|Shuo at MICCAI2019]]
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[[File:2020-SPIE-SH-Poster-1.jpg|200px|thumb|right|Shuo at SPIE-MI 2020]]
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[[File:Mmexport1554990645167~2.jpg|200px|thumb|right|Shuo at ISBI2019]]
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[[File:2019-MICCAI-Shuo-01.jpg|200px|thumb|right|Shuo at MICCAI 2019]]
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[[File:Addc.JPG|200px|thumb|right|Shuo at AC/DC]]
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[[File:Mmexport1554990645167~2.jpg|200px|thumb|right|Shuo at ISBI 2019]]
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[[File:Addc.JPG|200px|thumb|right|Shuo at AC/DC concert]]
 
[[File:Bottles.jpg|200px|thumb|right|Bottle collection of Shuo]]
 
[[File:Bottles.jpg|200px|thumb|right|Bottle collection of Shuo]]
  
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{{h5 | Image Analysis and Communications Laboratory, Johns Hopkins University, Baltimore, MD}}
 
{{h5 | Image Analysis and Communications Laboratory, Johns Hopkins University, Baltimore, MD}}
  
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* '''Cerebellar Parcellation and Analysis''' ''2016–present''
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* '''Surface reconstruction of subarachnoid space''' ''2020–present''
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* '''Super resolution of MRI''' ''2019–2020''
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* '''Cerebellar Parcellation and Analysis''' ''2016–2019''
  
 
{{h5 | Laboratory of Behavioral Neuroscience, NIA, NIH, Baltimore, MD}}
 
{{h5 | Laboratory of Behavioral Neuroscience, NIA, NIH, Baltimore, MD}}
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==Code and Packages==
 
==Code and Packages==
  
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{{h5 | Some documented publicly available packages}}
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* '''segviz''': 3D segmentation visualization. https://gitlab.com/shan-utils/segviz
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* '''improc3d''': 3D image transformation and operations. https://gitlab.com/shan-utils/improc3d
  
 
* '''dataset''': Deep learning data augmentation and operations. https://gitlab.com/shan-deep-networks/dataset
 
* '''dataset''': Deep learning data augmentation and operations. https://gitlab.com/shan-deep-networks/dataset
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* '''image-processing-3d''': 3D image transformation and operations. https://gitlab.com/shan-utils/image-processing-3d
 
  
 
* '''label-image-cleanup''': A simple label image cleanup based on connected components. https://gitlab.com/shan-utils/label-image-cleanup
 
* '''label-image-cleanup''': A simple label image cleanup based on connected components. https://gitlab.com/shan-utils/label-image-cleanup
  
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{{h5 | Singularity container images}}
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* '''ACAPULCO''': Cerebellum parcellation using convolutional neural networks. https://gitlab.com/shuohan/keras-unet-cerebellum
  
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* '''Cerebellum parcellation''': [[Cerebellum CNN|Instructions]] and the {{iacl|~shuo/data/cerebellum-parcellation.simg|Singularity image}}.
 
 
==Activities==
 
==Activities==
  
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==Publications==
 
==Publications==
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* '''Shuo Han''', Samuel Remedios, Aaron Carass, Michael Schär, Jerry L Prince. "MR Slice Profile Estimation by Learning to Match Internal Patch Distributions". IPMI, 2021.
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* '''Shuo Han''', Yang An, Aaron Carass, Jerry L. Prince, Susan M. Resnick. "Longitudinal analysis of regional cerebellum volumes during normal aging". NeuroImage, 2020.
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* '''Shuo Han''', Jerry L. Prince, and Aaron Carass. "Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors". MICCAI workshop MLMI, 2020.
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* '''Shuo Han''', Aaron Carass, Yufan He, and Jerry L. Prince. "Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization". NeuroImage, 2020.
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* '''Shuo Han''', Jerry L. Prince, and Aaron Carass. "Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation". SPIE, 2020.
  
 
* '''Shuo Han''', Aaron Carass, and Jerry L. Prince. "Hierarchical parcellation of the cerebellum." MICCAI, 2019.
 
* '''Shuo Han''', Aaron Carass, and Jerry L. Prince. "Hierarchical parcellation of the cerebellum." MICCAI, 2019.
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* '''Shuo Han''' and Daniel A. Herzka, "Polyhedral phantom framework with analytical Fourier transform with intensity gradients." presented at ISMRM 23rd Meeting, Toronto, Canada, 2015.
 
* '''Shuo Han''' and Daniel A. Herzka, "Polyhedral phantom framework with analytical Fourier transform with intensity gradients." presented at ISMRM 23rd Meeting, Toronto, Canada, 2015.
  
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* Hao Li, Can Zhao, '''Shuo Han''', Aaron Carass, and Jerry L. Prince. "A resolution measurement algorithm for MRI." presented at ISBI 2019.
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* Hao Li, Can Zhao, '''Shuo Han''', Aaron Carass, and Jerry L. Prince. "A resolution measurement algorithm for MRI." presented by Shuo Han at ISBI 2019.

Latest revision as of 00:52, 13 July 2021

Shuo at SPIE-MI 2020
Shuo at MICCAI 2019
Shuo at ISBI 2019
Shuo at AC/DC concert
Bottle collection of Shuo

Education

Johns Hopkins University, Baltimore, MD 2016–present

  • Doctor of Philosophy in Biomedical Engineering

Johns Hopkins University, Baltimore, MD 2014–2016

  • Master’s of Science and Engineering in Biomedical Engineering

Tsinghua University, Beijing, China 2010–2014

  • Bachelor of Engineering in Biomedical Engineering
  • Minor in Computer Science & Technology

Research

Image Analysis and Communications Laboratory, Johns Hopkins University, Baltimore, MD
  • Surface reconstruction of subarachnoid space 2020–present
  • Super resolution of MRI 2019–2020
  • Cerebellar Parcellation and Analysis 2016–2019
Laboratory of Behavioral Neuroscience, NIA, NIH, Baltimore, MD
  • Longitudinal Analysis of the Cerebellum 2016–2019
Dynamic Imaging Laboratory, Johns Hopkins University School of Medicine, Baltimore, MD
  • Cardiac MRI Heartbeat-Type Triggering System During Arrhythmia 2015–2016
  • Polyhedral Phantom Framework with Analytical Fourier Transform 2014–2016
Tsinghua University, Center for Biomedical Imaging Research, Beijing, China
  • Unsupervised Machine Learning Based Motion Compensation in MRI 2013–2014

Code and Packages

Activities

  • CHAOS ISBI 2019 challenge the second place in Tasks 3 and 5.

Publications

  • Shuo Han, Samuel Remedios, Aaron Carass, Michael Schär, Jerry L Prince. "MR Slice Profile Estimation by Learning to Match Internal Patch Distributions". IPMI, 2021.
  • Shuo Han, Yang An, Aaron Carass, Jerry L. Prince, Susan M. Resnick. "Longitudinal analysis of regional cerebellum volumes during normal aging". NeuroImage, 2020.
  • Shuo Han, Jerry L. Prince, and Aaron Carass. "Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors". MICCAI workshop MLMI, 2020.
  • Shuo Han, Aaron Carass, Yufan He, and Jerry L. Prince. "Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization". NeuroImage, 2020.
  • Shuo Han, Jerry L. Prince, and Aaron Carass. "Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation". SPIE, 2020.
  • Shuo Han, Aaron Carass, and Jerry L. Prince. "Hierarchical parcellation of the cerebellum." MICCAI, 2019.
  • Shuo Han, Yufan He, Aaron Carass, Sarah H. Ying, and Jerry L. Prince. "Cerebellum parcellation with convolutional neural networks." SPIE, 2019.
  • Shuo Han, and Daniel A. Herzka. "Software Framework for Realistic MRI Simulations Using the Polyhedral Fourier Transform." SASHIMI, 2016.
  • Lianrui Zuo, Shuo Han, Aaron Carass, Sarah H. Ying, Chiadikaobi U. Onyike, and Jerry L. Prince. "Automatic quality control using hierarchical shape analysis for cerebellum parcellation." SPIE, 2019.
  • Muhan Shao, Shuo Han, Aaron Carass, Xiang Li, Ari M. Blitz, Jaehoon Shin, Jerry L. Prince, and Lotta M. Ellingsen. "Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly." NeuroImage: Clinical, 2019
  • Muhan Shao, Shuo Han, Aaron Carass, Xiang Li, Ari M. Blitz, Jerry L. Prince, and Lotta M. Ellingsen. "Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks." MICCAI workshop DLF (Deep Learning Fails), 2018.
  • Aaron Carass, Jennifer L. Cuzzocreo, Shuo Han, Carlos R. Hernandez-Castillo, Paul E. Rasser, Melanie Ganz, Vincent Beliveau, et al. "Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images." NeuroImage, 2018.
  • Lianrui Zuo, Aaron Carass, Shuo Han, and Jerry L. Prince. "Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation." SPIE, 2018.
  • Tri M. Ngo, George S. K. Fung, Shuo Han, Min Chen, Jerry L. Prince, Benjamin M. W. Tsui, Elliot R. McVeigh, and Daniel A. Herzka, “Realistic analytical polyhedral MRI phantoms.” Magnetic Resonance in Medicine, 2015.
  • Shuo Han and Daniel A. Herzka, "PolyFT: a Freely-available optimized MATLAB implementation of the polyhedral Fourier transform for analytical simulations in MRI." presented at ISMRM 24th Meeting, Singapore, 2016.
  • Shuo Han and Daniel A. Herzka, "Polyhedral phantom framework with analytical Fourier transform with intensity gradients." presented at ISMRM 23rd Meeting, Toronto, Canada, 2015.
  • Hao Li, Can Zhao, Shuo Han, Aaron Carass, and Jerry L. Prince. "A resolution measurement algorithm for MRI." presented by Shuo Han at ISBI 2019.
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