Shuo
Contents
- 1 Shuo Han 韩烁
- 2 Education
- 3 Research
- 3.1 Image Analysis and Communications Laboratory, Johns Hopkins University, Baltimore, MD
- 3.2 Laboratory of Behavioral Neuroscience, NIA, NIH, Baltimore, MD
- 3.3 Dynamic Imaging Laboratory, Johns Hopkins University School of Medicine, Baltimore, MD
- 3.4 Tsinghua University, Center for Biomedical Imaging Research, Beijing, China
- 4 Code and Packages
- 5 Activities
- 6 Publications
Shuo Han 韩烁
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
Cerebellar Parcellation and Analysis 2016–present
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
Some documented publicly available packages
dataset: Deep learning data augmentation and operations. https://gitlab.com/shan-deep-networks/dataset
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
Singularity container images
Upcoming
Activities
CHAOS ISBI 2019 challenge the second place in Tasks 3 and 5.
Publications
Shuo Han, Aaron Carass, and Jerry L. Prince. "Hierarchical parcellation of the cerebellum." MICCAI, 2019.
Hao Li, Can Zhao, Shuo Han, Aaron Carass, and Jerry L. Prince. "A resolution measurement algorithm for MRI." ISBI, 2019.
Shuo Han, Yufan He, Aaron Carass, Sarah H. Ying, and Jerry L. Prince. "Cerebellum parcellation with convolutional neural networks." SPIE, 2019.
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.
Shuo Han, and Daniel A. Herzka. "Software Framework for Realistic MRI Simulations Using the Polyhedral Fourier Transform." SASHIMI, 2016.
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.
