1. Multiple Organ Detection in CT Volumes using CNN Week 2
REU Student: Elizabeth Cole
Mentor: Sarfaraz Hussein
Our project

2. Project Overview Organ detection using 3D CNN
Extend to fat quantification; tumor detection
Steps:
Organ annotation
Train 2D CNN on CT slice patches
Train 3D CNN on CT volumes
Perform a confidence fusion

3. Organ Annotations in Amira
Liver -> all 4 organs
10/200 done; continually working on
Load stack of CT images for each person
Attach “OrthoSlices” for all three axes
Change contrast to highlight organs
For each axis, adjust bounding box
Save bounding box coordinates in Excel

4. Example Liver Annotation

5. MatConvNet Pretrained Model
Oxford: “Very Deep Convolutional Networks for Large-Scale Visual Recognition” (ICLR 2015)
Caffe: deep learning framework by UC Berkeley Vision
Trained on over 1.2 million images
16 layers

6. MatConvNet Classifications

7. Deep CNN & Lymph Node Detection
Use “DropConnect” to avoid overfitting; behaves as a regularizer when training
CNN model = 9-12 hours training
Increased classification sensitivity: 55-70% in the mediastinum; 30-83% in abdomen
Roth, Holger, Le Lu, Ari Seff, Kevin Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, and Ronald Summers. "A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations."Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Lecture Notes in Computer Science Volume 8673, 2014, Pp N.p., Web. 01 June 2015.

8. DropConnect Dept. of CS at NYU
Purpose: regularize large fully-connected layers in neural nets
Sets a randomly selected subset of weights in network to zero

9. Handcrafted & CNN: Breast Cancer
Goal: mitosis detection using both methods
CNN models = “domain agnostic”
Handcrafted features such as shape, structure, color, and texture = application specific
Combination:
Less computationally demanding
Maximize performance
Minimized false positives with high precision
2. Haibo Wang; Angel Cruz-Roa; Ajay Basavanhally; Hannah Gilmore; Natalie Shih, et al. "Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features", J. Med. Imag. 1(3), (Oct 10, 2014).

10. True Positives/False Neg/False Pos.

11. Region-based CNN & Object Detection
Multi-layer convolutional networks
Improves mean avg. precision by over 50% compared to best results on PASCAL VOC 2012
CNNs combined with supervised pre-training
Must generalize classification to detection
Relevant to organ detection goal
3. Girshick, Ross, J. Donahue, T. Darrell, and J. Malik. "Region-based Convolutional Networks for Accurate Object Detection and Segmentation." IEEE Xplore. N.p., 25 May Web. 05 June 2015.

12. Future Work Apply MatConvNet to actual CT data
Continue working on organ annotations
Train 2D and 3D CNN
Confidence fusion

13. References
Roth, Holger, Le Lu, Ari Seff, Kevin Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, and Ronald Summers. "A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations."Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Lecture Notes in Computer Science Volume 8673, 2014, Pp N.p., Web. 01 June 2015.
Haibo Wang ; Angel Cruz-Roa ; Ajay Basavanhally ; Hannah Gilmore ; Natalie Shih, et al. "Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features", J. Med. Imag. 1(3), (Oct 10, 2014).
Girshick, Ross, J. Donahue, T. Darrell, and J. Malik. "Region-based Convolutional Networks for Accurate Object Detection and Segmentation." IEEE Xplore. N.p., 25 May Web. 05 June 2015.
Regularization of Neural Network using DropConnect Li Wan, Matthew Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus. International Conference on Machine Learning 2013

14. Questions/Comments?