Welcome to Hanbo Chen’s homepage.

I am a research scientist at a medical A.I. company CuraCloud. My specialty is to develop deep learning algorithms for smart medical system. I pursued my Ph.D degree in computer science under the supervision of Dr. Tianming Liu at University of Georgia. My Ph.D thesis centered on understanding the architecture of brain connectome. Following it, I developed a set of smart computational methods to tackle challenges associated with big neuroimaging data.

Smart Computing

Machine Learning algorithms.
Deep Learning algorithms.
High performance computationa platform.
3D+ visualization and interactive user interface.


Different Problems

Analyze large-scale, group-wise, multi-scale, multi-model, across species neuroimages to understand brain function, structure, evolution, development, and diseases.


Rich Expericences

C/C++, Matlab, Python, Qt, Spark.
Structural/functional/diffusion MRI.
Neuron trace/microscopy images.
Human, primate, mouse, drosophila brains.


Simple Numbers

25 First-authored Publications
4 Released Programs
1 Goal

Programs and Softwares

I developed a set of smart computational methods to tackle various challenges associated with big neuroimaging data. Some are implemented as plugins of Vaa3D which is a publicly available open source platform with user-friendly interface for 3D+ image analysis and visualization. Some are implemented in C++, matlab, or python.

Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.

Key features: (1) automatic speed matching and alignment, (2) 3D+ interactive operation, (3) performance intensively evaluated, (4) the only publicly available software for this task.


NeuronStitcher [site, news]C++, Vaa3D plugin

Multi-view spectral clustering fuse graph topology in eigenspace to maximize the agreement between different views (e.g. different dimensions, modalities, or features). We designed this algorithm to handle the substantial variability of large-scale brain networks across modalities (DTI and R-fMRI) and different individuals. It has been successfully applied to investigate group-wisely commen normal brain networks and brain network abnormalty in mTBI subjects.

Key features: graph fusion, graph clustering


multi-view spectral clustering
[codes, paper, mTBI study] Matlab

SmartTracing is a noval machine learning neuron tracing framework that does not require substantial human intervention to reconstruct neuron morphology from 3D microscopy images.

Key features: (1) It automatically identifies reliable portions of a neuron reconstruction generated by some existing neuron-tracing algorithms, without human intervention. (2) From the training exemplars the most characteristic wavelet features (3D sepherical wavelet) are automatically selected (mRMR) and used in a machine-learning framework (SVM) to predict all image areas that most probably contain neuron signal to improve the performance of an existing automatic tracing method.


SmartTracing [paper]C++, SVM, wavelet, Vaa3D plugin

The high dimensionality of 3D brain image and the big data from large study samples are the major challenge for a comprehensive analysis of brain functional archetecture. We proposed a high performance computing platform - Apache Spark-enabled functional network informatics platform, to analysis and cluster common functional networks among over 30,000 volumetric images.

Key features: high-performance computer cluster computation for big volumetric images based on Spark.


large-scale brain image clustering
[paper, site] Python, Spark, 3D volume clustering

Research Projects


My Ph.D thesis centers on one theme: understanding the architecture of brain connectome. Following this theme, I studied different aspects of the brain network by using various imaging modalities, analyzed different animal models, and developed a set of smart computational methods to tackle various challenges associated with big neuroimaging data.
Click here to view a complete summary of my researches in pdf.

Group-wise analysis between different populations

Defining reliable, reproducible and accurate brain regions of interest is the first and fundamentally important step in performing group-wise analysis of brain connectome. Facing this challenge, my current lab directed by Prof. Tianming Liu has made great progress on defining brain ROIs in the past 5 years and a good portion of my research contributes to this trend. [Related projects: DICCCOL, HAFNI]

Cross validate findings between resolution scales

Advanced neuroimaging techniques allow researchers to investigate brain on different scales (molecular-scale, micro-scale, meso-scale, and macro-scale) which offer complementary pictures of the brain. Coarser scale images enable a global and population-wise view of the brain, while finer scale images carry more details and can be used to validate findings on coarser scales. [Related projects: Brain Decoding Project, multiscale brain modeling]

Link functional and structural connectome

If we view brain as a computer, then the structural (anatomical) connectome is the hardware of brain and the functional connectome is the software in brain. This project attempts to view brain from both aspects for a comprehensive understanding of brain. [Related projects: Brain Decoding Project, DICCCOL]

Link findings across species

The rationale of studying and comparing neuroimaging data across species are mainly two-fold: 1) the differences across species allow us to investigate how brain evolves and how human-specific brain functions emerge; 2) the common brain mechanisms and anatomical regions across species allow us to investigate human brain based on animal models. [Related projects: cortical development study, multiscale brain modeling]

High-throughput computing for neuroimages

With the increasing image resolution and dataset size, the lack of efficient computational pipelines is becoming the bottleneck that slows the findings in neuroscience field. To increase the throughput, two features are very important for tool design: 1) ‘smart’; 2) high-performance computing. [Related projects: Big Neuron Project]


Pdf version of my CV is available here: [pdf].


PhD Student in Computer Science
2010 – Present
Computer Science Department, the University of Georgia, U.S.A
GPA: 3.98
Major Professor: Dr. Tianming Liu.
Advisory Committee: Dr. Dinggang Shen, Dr. Hanchuan Peng, Dr. Suchi Bhandarkar, Dr. Qun Zhao
Bachelor of Science in Electronics Engineering
2006 – 2010
Northwestern Polytechnical University (NPU), Xi’an, P.R. China
GPA:90.6/100 Ranking: 2nd; Thesis: Cortical development study based on deformable surface model and spherical wavelet


Program Language:
C++, C#, JAVA, MATLAB, assemble language, VB, UML, sql;
Machine Learning Algorithms:
(multi-view) spectral clustering, dictionary learning and sparse coding, deep learning;
Program Lib:
vtk, QT, FSL, GSL, MATLAB Engine, openCV, openGL;
Biomedical imaging tool:
FSL, Vaa3D, Hammer, Freesurfer, MedINRIA, TrackVis, MRICro, mipav, paraview, imageJ, Photoshop;
Biology skills:
drosophilia larvae dissection, tissue staining, EM volume tracing;
Other Skills:
Sketching, Painting, Graphic Design.


Research Assistant
Aug. 2010 – Present
CAID Lab, the University of Georgia, U.S.A
Director: Dr. Tianming Liu.
Visiting Student
Oct. 2014 - July 2015
Hanchuan Peng's Team, Allen Institute for Brain Science, U.S.A
Director: Dr. Hanchuan Peng.
Visiting Student Researcher
Feb. - Mar. 2013
Cardona Lab, HHMI Janelia Farm Research Campus, U.S.A
Director: Dr. Albert Cardona.
Visiting Student
July 2012
IDEA Lab, the University of North Carolina at Chapel Hill, U.S.A
Director: Dr. Dinggang Shen.
Research Assistant
Aug. 2009 – Jul. 2010
Biomedical Imaging and Analysis Joint Lab, NPU, Xi’an, P.R. China
Director: Dr. Lei Guo.
Program PI, Project Developer
Oct. 2007 – May 2009
National Innovation Experiment Program for Undergraduates, P. R. China
Project: Online Compiler. Advisor: Xuefeng Jiang.

Invited Talk

Introduction to DICCCOL
Feb. 21th 2014
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, P. R. China
Invited Talk.
Introduction to DICCCOL
Feb. 20th 2014
Peking University Sixth Hospital, P. R. China
Invited Talk.
Introduction to Connectome
June. 8th 2013
School of Automation, Nanjing University of Science and Technology, P. R. China
Invited Talk.
Introduction to Connectome
May. 20th 2013
School of Computer Science, Nanjing University of Aeronautics and Astronautics, P. R. China
Invited Talk.

Honors and Awards

May. 2014
Chinese Government Award for Outstanding Self-financed Students Abroad
Oct. 2013
Franklin Foundation Travel Award


Since I was child, I have great interest in painting and design. I design logos. I paint digitally and on canvas. I also tried to make every picture in my paper a piece of art. I showed some of them here. Enjoy :)

Copyright: Hanbo Chen



Hanbo Chen*, Yujie Li*, Fangfei Ge, Gang Li, Dinggang Shen, Tianming Liu, Gyral Net: A New Representation of Cortical Folding Organization, Medical Image Analysis, 2017. in press. *Co-first authors
Hanbo Chen, Daniel Maxim Iascone, Nuno Maçarico da Costa, Ed S. Lein, Tianming Liu, Hanchuan Peng, Fast assembling of neuron fragments in serial 3D sections, Brain Informatics, 2017. vol. 4(3), pp. 183-186.
Hanbo Chen, Hang Xiao, Tianming Liu, Hanchuan Peng, SmartTracing: Self-learning based Neuron Reconstruction, Brain Informatics, 2015. vol. 2(3), pp. 135-144.
Hanbo Chen, Tao Liu, Yu Zhao, Tuo Zhang, Yujie Li, Meng Li, Hongmiao Zhang, Hui Kuang, Lei Guo, Joe Tsien, Tianming Liu, Optimization of Large-scale Mouse Brain Connectome via Joint Evaluation of DTI and Neuron Tracing Data, NeuroImage, 2015. vol. 115, pp. 202-213.
Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, Yixuan Yuan, Peili Lv, Tuo Zhang, Lei Guo, Dinggang Shen*, Tianming Liu*. Inferring Group-wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering, IEEE Transactions on Medical Imaging, 2013. vol. 32(9), pp. 1576-1586. *Joint corresponding authors.
Hanbo Chen*, Tuo Zhang*, Lei Guo, Kaiming Li, Xiang Yu, Longchuan Li, Xintao Hu, Junwei Han, Xiaoping Hu**, Tianming Liu**, Coevolution of Gyral Folding and Structural Connection Patterns in Primate Brains, Cerebral Cortex, 2013. vol. 23(5), pp. 1208-1217. *Joint First Authors, **Co-corresponding Authors.
Yujie Li*, Hanbo Chen*, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joe Z. Tsien, Tianming Liu, Discover Mouse Gene Co-expression Landscapes Using Dictionary Learning and Sparse Coding, Brain Structure and Function, 2017. in press. *Co-first authors
Yujie Li*, Hanbo Chen*, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z. Tsien, Tianming Liu, Transcriptome architecture of adult mouse brain revealed by sparse coding of genome-wide in situ hybridization images, Neuroinformatics, 2017. vol. 15(3), pp. 285-295. *Co-first authors
Tuo Zhang*, Hanbo Chen*, Lei Guo, Kaiming Li, Longchuan Li, Shu Zhang, Dinggang Shen, Xiaoping Hu**, Tianming Liu**, Characterization of U-shape Streamline Fibers: Methods and Applications, Medical Image Analysis, 2014. vol. 18(5), pp. 795-807. *Joint first authors, **Joint corresponding authors.
Xiao Li*, Hanbo Chen*, Tuo Zhang*, Xiang Yu, Xi Jiang, Kaiming Li, Longchuan Li, Mir Jalil Razavi, Xianqiao Wang, Xintao Hu, Junwei Han, Lei Guo, Xiaoping Hu**, Tianming Liu**, Evolutionarily-Preserved and Species-Specific Gyral Folding Patterns across Primate Brains, Brain Structure and Function, 2017. vol. 222(5), pp. 2127–2141. *Co-first authors, **Joint-corresponding authors.
Yu Zhao*, Hanbo Chen*, Yujie Li, Jinglei Lv, Xi Jiang, Xiang Li, Fangfei Ge, Tuo Zhang, Shu Zhang, Bao Ge, Cheng Lyu, Shijie Zhao, Junwei Han, Lei Guo, Tianming Liu, Connectome-scale Group-wise Consistent Resting-state Network Analysis in Autism Spectrum Disorder, NeuroImage: Clinical, 2016. vol. 12, pp. 23-33. *Co-first authors
Armin Iraji*, Hanbo Chen*, Natalie Wiseman, Tuo Zhang, Robert D Welch, Brian J ONeil, Syed Ayaz, Xiao Wang, Conor Zuk, E Mark Haacke, Tianming Liu, Connectome-scale Assessment of Structural and Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage, NeuroImage: Clinical, 2016. vol. 12, pp. 100-115. *Co-first authors.

Conference Proceedings

(accept rate of these conferences are 20%-30%)

Hanbo Chen, Yujie Li, Yu Zhao, Jinglei Lv, Tianming Liu, Inter-subject fMRI registration based on functional networks, ISBI, 2017, pp. 863-867.
Yujie Li*, Hanbo Chen*, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng**, Joe Z. Tsien**, Tianming Liu**, Discover Mouse Gene Coexpression Landscape Using Dictionary Learning and Sparse Coding, MICCAI, LNCS, 2016, vol. 9900, pp. 63-71. *Co-first authors; **Joint corresponding authors.
Hanbo Chen*, Armin Iraji*, Xi Jiang, Jinglei Lv, Zhifeng Kou**, Tianming Liu**, Longitudinal Analysis of Brain Recovery After Mild Traumatic Brain Injury Based on Groupwise Consistent Brain Network Clusters, MICCAI 2015, LNCS, vol. 9350, pp. 194-201. *Co-first authors; **Joint corresponding authors.
Yue Yuan*, Hanbo Chen*, Jianfeng Lu, Tuo Zhang, Tianming Liu, Distance Networks for Morphological Profiling and Characterization of DICCCOL Landmarks, MICCAI 2015, LNCS, vol. 9350, pp. 380-387. *Co-first authors.
Hanbo Chen, Yu Zhao, Tuo Zhang, Hongmiao Zhang, Hui Kuang, Meng Li, Joe Z. Tsien, Tianming Liu, Construct and Assess Multimodal Mouse Brain Connectomes via Joint Modeling of Multi-scale DTI and Neuron Tracer Data, MICCAI 2014, LNCS, vol. 8675, pp. 273-280.
Hanbo Chen, Kaiming Li, Dajiang Zhu, Lei Guo, Tianming Liu. Group-wise Optimization and Individualized Prediction of Structural Connectomes, ISBI 2014, pp. 742-745.
Hanbo Chen*, Xiang Yu*, Xi Jiang, Kaiming Li, Longchuan Li, Xintao Hu, Junwei Han, Lei Guo, Xiaoping Hu**, Tianming Liu**. Evolutionarily-preserved Consistent Gyral Folding Patterns Across Primate Brains, ISBI 2014, pp. 1218-1221. *Co-first authors; **Joint corresponding authors.
Hanbo Chen, Tuo Zhang, Tianming Liu. Identifying Group-wise Consistent White Matter Landmarks via Novel Fiber Shape Descriptor, MICCAI 2013, LNCS, vol. 8149, pp. 66-73.(Oral, 5%)
Hanbo Chen, Kaiming Li, Dajiang Zhu, Tianming Liu. Identifying Consistent Brain Networks via Maximizing Predictability of Functional Connectome from Structural Connectome, ISBI 2013. pp. 978-981.
Xiang Yu*, Hanbo Chen*, Tuo Zhang, Xintao Hu, Lei Guo, Tianming Liu. Joint Analysis of Gyral Folding and Fiber Shape Patterns, ISBI 2013. pp. 85 - 88. *Joint First Author.
Hanbo Chen, Kaiming Li, Dajiang Zhu, Tuo Zhang, Changfeng Jin, Lei Guo, Lingjiang Li, Tianming Liu. Inferring Group-wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering, MICCAI 2012, LNCS, vol. 7512, pp. 297-304.
Hanbo Chen, Xiao Cai, Dajiang Zhu, Feiping Nie, Tianming Liu, Heng Huang. Group-wise Consistent Parcellation of Gyri via Adaptive Multi-view Spectral Clustering of Fiber Shapes, MICCAI 2012, LNCS, vol. 7511, pp. 271-279.
Hanbo Chen, Lei Guo, Kaiming Li, Xintao Hu, Tianming Liu. Assessment of Regularity and Variability of Cortical Folding Patterns of Working Memory ROIs, MICCAI 2011, LNCS, vol. 6892, pp. 318-326.
Hanbo Chen, Lei Guo, Jingxin Nie, Tuo Zhang, Xintao Hu, Tianming Liu. A dynamic skull model for simulation of cerebral cortex folding, MICCAI 2010, LNCS, vol. 6362, pp. 412-419.

Conference Abstracts

NeuronStitcher: A Suite for Stitching Neuron Fragments in Serial 3D Sections, SfN, 2015.
Group-wise Optimization and Individualized Prediction of Structural Connectomes, OHBM, 2013.
Inferring Group-wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering, OHBM, 2013.

Coauthored Papers

Armin Iraji, Hanbo Chen, Natalie Wiseman, E. Mark Haacke, Tianming Liu, Zhifeng Kou, Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury, Neural Plasticity, 2016. vol. 2016, pp. 4072402.
Tuo Zhang, Hanbo Chen, Lei Guo, Kaiming Li, Longchuan Li, Shu Zhang, Dinggang Shen, Xiaoping Hu, Tianming Liu, Characterization of U-shape Streamline Fibers: Methods and Applications. Medical Image Analysis, 2014. vol. 18(5), pp. 795-807.
Tao Zeng, Hanbo Chen, Ahmed Fakhry, Xiaoping Hu, Tianming Liu*, Shuiwang Ji*, Allen Mouse Brain Atlases Reveal Different Neural Connection and Gene Expression Patterns in Cerebellum Gyri and Sulci, Brain Structure and Function, 2015. vol. 220(5), pp. 2691-703. *Joint-correspondence authors.
Xin Zhang, Lei Guo, Xiang Li, Tuo Zhang, Dajiang Zhu, Kaiming Li, Hanbo Chen, Jinglei Lv, Changfeng Jin, Qun Zhao, Lingjiang Li, Tianming Liu. Characterization of Task-free and Task-performance Brain States via Functional Connectome Patterns, Medical Image Analysis, 2013. vol. 17(8), pp. 1106–1122.
Yixuan Yuan, Xi Jiang, Dajiang Zhu, Hanbo Chen, Kaiming Li, Peili Lv, Xiang Yu, Xiaojin Li, Shu Zhang, Tuo Zhang, Xintao Hu, Junwei Han, Lei Guo, Tianming Liu, Meta-analysis of Functional Roles of DICCCOLs, Neuroinformatics, 2013. vol. 11(1), pp. 47-63.
Degang Zhang, Lei Guo, Dajiang Zhu, Kaiming Li, Longchuan Li, Hanbo Chen, Qun Zhao, Xiaoping Hu**, and Tianming Liu**, Diffusion Tensor Imaging Reveals Evolution of Primate Brain Architectures, accpeted, Brain Structure and Function, 2012. **Joint corresponding authors.
Kaiming Li, Lei Guo, Carlos Faraco, Dajiang Zhu, Hanbo Chen, Yixuan Yuan, Jinglei Lv, Fan Deng, Xi Jiang, Tuo Zhang, Xintao Hu, Degang Zhang, Lloyd Miller, Tianming Liu, Visual Analytics of Brain Networks, NeuroImage, 2012. vol. 61(1), pp. 82–97.
Dajiang Zhu*, Kaiming Li*, Lei Guo, Xi Jiang, Tuo Zhang, Degang Zhang, Hanbo Chen, Fan Deng, Carlos Faraco, Changfeng Jin, Chong-Yaw Wee, Yixuan Yuan, Peili Lv, Yan Yin, Xiaolei Hu, Lian Duan, Xintao Hu, Junwei Han, Lihong Wang, Dinggang Shen, L Stephen Miller, Lingjiang Li, Tianming Liu, DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks, *Joint first authors, Cerebral Cortex, 2013. vol. 23(4), pp. 786-800.
Jingxin Nie, Lei Guo, Kaiming Li, Yonghua Wang, Guojun Chen, Longchuan Li, Hanbo Chen, Fan Deng, Xi Jiang, Tuo Zhang, Ling Huang, Carlos Faraco, Degang Zhang, Cong Guo, Pew-Thian Yap, Xintao Hu, Gang Li, Jinglei Lv, Yixuan Yuan, Dajiang Zhu, Junwei Han, Dean Sabatinelli, Qun Zhao, L Stephen Miller, Bingqian Xu, Ping Shen, Simon Platt, Dinggang Shen, Xiaoping Hu, Tianming Liu, Axonal Fiber Terminations Concentrate on Gyri, Cerebral Cortex, 2012. vol. 22(12), pp. 2831-2839.
Dajiang Zhu, Kaiming Li, Carlos Faraco, Fan Deng, Degang Zhang, Xi Jiang, Hanbo Chen, Lei Guo, Stephen Miller, Tianming Liu, Optimization of Functional Brain ROIs via Maximization of Consistency of Structural Connectivity Profiles, NeuroImage, 2012. vol. 59(2), pp. 1382–1393.