1. Multiple Organ Detection in CT Volumes using CNN Week 1
REU Student: Elizabeth Cole
Mentor: Sarfaraz Hussein

2. Goal Detect multiple organs automatically Liver, heart, kidneys, etc.
CT volumes
Three dimensional CT (computed tomography) images
Image Credit: NIH website

3. Motivation Medical diagnosis Treatment/Radiotherapy planning
Fat quantification
Organ segmentation

4. Definitions
Image segmentation - process of partitioning a digital image into multiple segments
Superpixel - the multiple segments, or sets of pixels an image is divided into
Supervoxel - 3-D segments of a volume

5. Super-Pixel Segmentation
Image Credit: Ref. 1

6. Software ITK-SNAP Free 3D medical image segmentation
MRI and CT data sets
Potentially working with 2,000 images

7. Amira 3D/4D imaging software More powerful
FEI Visualization Sciences Group
Expensive
Broader applications

8. Organ annotations
Bounding boxes around organs
“Ground truth”

9. Approach Convolutional Neural Networks
Training and Testing on 2D patches and 3D volumes
Conditional Random Fields (CRF)

10. Neural Networks Can computers think more like people?
Image Credit: Ref. 2

11. Weighted Inputs
Image Credit: Ref. 2

12. Back Propagation Process
Random weights
Gradient descent learning method
Which nodes are most to blame?
Improve weights
Try again!

13. Convolutional Neural Networks
Neurons respond to regions of image
Act directly on raw inputs
Automate feature construction
Interested in 2D and 3D
Increasingly complex deep learning

14. 3D CNN + Human Action Recognition
Goal: action classification, no assumptions
No handcrafted features
Temporal dimension
3D kernel on stacks of multiple frames
“3D Convolutional Neural Networks for Human Action Recognition” by Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu

15. 3D CNN Superiority
FPR = False Positive Rate

16. Neural Network Implementation
Input: a, b, c in quadratic formula
Output is 1 if two real roots; -1 if not
Trained on 1000 data sets
Tested on 200 data sets
Learned the correct weights for each node

17. Neural Networks vs. SVM SVM is faster Neural nets more accurate
Percent Error
Time (sec)
Neural Networks 0%
SVM 5%
0.0156
SVM is faster
Neural nets more accurate
Convolutional neural nets better at image recognition

18. Future Steps Annotating where organs are
Improve ITK-SNAP and Amira skills
Using CNN in Matlab
Train 2D CNN on CT slice patches
Train 3D CNN on CT volumes
Perform a confidence fusion

19. References
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical Report , June 2010.
Suarez, Jesus. "Intro to Neural Networks." YouTube. N.p., 14 Dec Web. 27 May 2015.
Anguelov, Bobby. "Basic Neural Network Tutorial - Theory." Web log post. N.p., 3 Apr Web. 27 May 2015.
Karpathy, Andrej. "CS231n Convolutional Neural Networks for Visual Recognition." Stanford University, n.d. Web. 27 May 2015.
Ji, Shuiwang, Wei Xu, Ming Yang, and Kai Yu. "3D Convolutional Neural Networks for Human Action Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence (2013): Web. 28 May 2015.
"Alzheimer's Disease Neuroimaging Study Launched Nationwide by the National Institutes of Health." PsycEXTRA Dataset (2006): n. pag.Cornell University Library. 10 Feb Web. 28 May 2015.
Dundar, Aysegul. "Convolutional Neural Networks." YouTube. N.p., 13 Jan Web. 28 May 2015.

20. Questions/Comments?