1. RUIDA CHENG
ruidacheng@gmail.com (240) 330-7752
EDUCATION:
George Washington University University of Arizona
MS in Computer Science, Dec 2007 BS in Computer Science, May 2001
PROGRAMMING SKILLS: Java, C, GLSL, OpenGL, Cuda, OpenCL, Python
INTERESTS: Parallel processing, machine learning (deep learning), 3D rendering
WORK EXPERIENCE:
NIH April 2003 – Present (MD)
CIT – Biomedical Imaging Research Services Section (BIRSS) – MIPAV software.
Computer Scientist
Summary:
• Lead many scientific research projects from initial design to implementation. Lead the
research effort in MIPAV team.
• Pragmatic view of implementation realities.
• Communicate closely and efficiently with many NIH principle investigators.
• Motivated, dependable, and hard working.
• Utilize creative and novel techniques to address complex technical problems.
Projects:
1. Apply holistically-nested edge detection CNNs model for MRI prostate and knees
segmentation.
2. R&D on Cuda-Convnet2 and Caffe based Deep CNNs models for medical image
segmentation.
3. Combine eye tracker system with MIPAV multi-modality viewer for a realistic reading
room experience for the radiologists.
4. Develop a recursion based edge pattern detection algorithm for MRI knees segmentation.
5. Apply Cuda-Convnet deep learning as a refinement procedure into MRI based knees and
prostate segmentation.
6. Develop deep learning based prototype automatic MRI prostate segmentation algorithm
in two months, and achieve high segmentation accuracy.
7. Design and implement GPU multi-histogram volume rendering fly-thru navigation
system for virtual bronchoscopy airway tracking.
8. Develop machine learning based (Support Vector Machine, Active Appearance Model)
fully automatic segmentation algorithms for 3D prostate MRI images.
9. Develop multi-threaded registration algorithm for semi-automatic prostate segmentation.
10. Develop MIPAV GPU 3D visualization framework, which is based on Java OpenGL
(GPU, GLSL) and WildMagic game engine.
11. Owner of MIPAV 3D visualization component, the framework was integrated into
Phillips needle tracking research software.
12. Side applications include surface extraction and decimation, RFA simulation, Haptic
robotic device intervention, 3D visual endoscopy simulation, DTI visualization, etc.
13. Develop Nvidia 3D Vision shutter glasses based stereoscopic rendering framework.
14. Porting many algorithms from C++ to Java, such as 3D volume and surface rendering,
geometry, AAM, multi-class SVM, surface reconstruction, WildMagic library, etc.

2. IBM Jan 2001 – May 2002 (AZ)
Shark Group – Enterprise Storage Server MICROCODE, Platform OS team.
Software Engineer
1. Perform embedded coding on AIX kernel. Develop HRM (Hardware Resource
Management) finite state machine to configure and sync up hardware resources, which
include host adapters, CPI and device adapters.
2. Use locking mechanism (C) to implement the device driver read/write module, which
controls buffer transmission through multiple channels between kernel mode and user
mode. Create prototypes and function specification for variety of software projects.
3. Develop STATESAVE debugging tool (PERL embedded in TCL-TK GUI) to trace HRM
status. Implement user trace mechanism to save kernel trace space into user trace space.
PUBLICATIONS:
Deep Learning with Orthogonal Volumetric HED Prostate Segmentation and 3D Surface
Reconstruction Model of Prostate MRI, Ruida Cheng, Nathan Lay, Holger R. Roth, Le Lu, Baris
Turkbey, Francesca Mertan, William Gandler, Evan S. McCreedy, Peter Choyke, Matthew J.
McAuliffe, Ronald M. Summers, IEEE ISBI, 2017 (submitted).
Developing Eye Tracking Environment for Prostate Cancer Diagnosis Using Multi-parametric
MRI, Ulas Bagci, Haydar Celik, Baris Turkbey, Ruida Cheng, Evans S. McCreedy, Peter
Choyke, Matthew J. McAuliffe, Brad Wood, ISMRM 2017 (submitted).
1. Automatic MR Prostate Segmentation by Deep Learning with Holistically-Nested Networks,
Ruida Cheng, Holger R. Roth, Nathan Lay, Le Lu, Baris Turkbey, William Gandler, Evan S.
McCreedy, Peter Choyke, Ronald M. Summers, Matthew J. McAuliffe, SPIE Medical Imaging,
Feb 2017 (to appear).
2. Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image
Segmentation, Ulas Bagci, Haydar Celik, Baris Turkbey, Ruida Cheng, Evans S. McCreedy, Peter
Choyke, Matthew J. McAuliffe, Brad Wood, MICCAI workshop, Oct 2016.
3. Active Appearance Model and Deep Learning for More Accurate Prostate Segmentation on
MRI, Ruida Cheng, Holger R. Roth, Le Lu, Shijun Wang, Baris Turkbey, William
Gandler, Evan S. McCreedy, Harsh K. Agarwal, Peter Choyke, Ronald M. Summers,
Matthew J. McAuliffe, SPIE Medical Imaging, Feb 2016.
4. Patellar Segmentation from 3D Magnetic Resonance Images using Guided Recursive Ray-
tracing for Edge Pattern Detection, Ruida Cheng, Jennifer N. Jackson, Evan S. McCreedy,
William Gandler, JJFA Eijkenboom, M van Middelkoop, Matthew J. McAuliffe, Frances
T. Sheehan, SPIE Medical Imaging, Feb 2016.
5. GPU-based Multi-Histogram Volume Navigation for Virtual Bronchoscopy, Ruida Cheng,
Sheng Xu, Alexandra Bokinsky, Evan McCreedy, Bradford J. Wood, Matthew McAuliffe, IEEE
EMBC, Aug 2014.
6. Atlas Based AAM and SVM Model for Fully Automatic MRI Prostate Segmentation, Ruida
Cheng, Baris Turkbey, William Gandler, Harsh K. Agarwal, Vijay P. Shah, Alexandra Bokinsky,
Evan McCreedy, Shijun Wang, Sandeep Sankineni, Marcelino Bernardo, Thomas Pohida, Peter
Choyke, Matthew McAuliffe, IEEE EMBC, Aug 2014.
7. Segmentation and Surface Reconstruction Model of Prostate MRI to Improve Prostate Cancer
Diagnosis, Ruida Cheng, Marcelino Bernardo, Justin Senseney, Alexandra Bokinsky, William
Gandler, Baris Turkbey, Thomas Pohida, Peter Choyke, Matthew McAuliffe, IEEE ISBI,
April 2013.

3. 8. 2D Registration Guided Models for Semi-automatic MRI Prostate Segmentation, Ruida Cheng,
Baris Turkbey, Justin Senseney, Alexandra Bokinsky, William Gandler, Evan McCreedy,
Thomas Pohida, Peter Choyke, Matthew McAuliffe, SPIE Medical Imaging, Feb 2013.
9. Java Multi-Histogram Volume Rendering Framework for Medical Images, Justin Senseney,
Alexandra Bokinsky, Ruida Cheng, Evan McCreedy, Matthew McAuliffe, SPIE Medical
Imaging, Feb 2013.
10. A flexible Java GPU-enhanced visualization framework and its applications, Ruida Cheng,
Alexandra Bokinsky, Justin Senseney, Nish Pandya, Evan McCreedy, Matthew McAuliffe, IEEE
CBMS 2012, June 2012.
11. Java based volume rendering frameworks (Conference Proceedings Paper), R. Cheng, et al.,
SPIE Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, March
2008.
12. Technologies for guidance of radiofrequency ablation in the multimodality interventional
suite of the future, Bradford J Wood, Julia K Locklin, Anand Viswanathan, Jochen Kruecker,
Dieter Haemmerich, Juan Cebral, Ariela Sofer, Ruida Cheng, Evan McCreedy, Kevin Cleary,
Matthew McAuliffe, Neil Glossop, Jeff Yanof, J Vasc Interv Radiol. 2007 Jan ;18 (1):9-24
13. Radio frequency ablation: registration, segmentation, and fusion tool, Evan S McCreedy,
Ruida Cheng, Paul F Hemler, Anand Viswanathan, Bradford J Wood, Matthew McAuliffe, IEEE
Trans Inf Technol Biomed. 2006 Jul ;10 (3):490-6