Multiple Organ Detection in CT Volumes using CNN Week 1

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Presentation transcript:

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

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

Motivation Medical diagnosis Treatment/Radiotherapy planning Fat quantification Organ segmentation

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

Super-Pixel Segmentation Image Credit: Ref. 1

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

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

Organ annotations Bounding boxes around organs “Ground truth”

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

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

Weighted Inputs Image Credit: Ref. 2

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

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

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

3D CNN Superiority FPR = False Positive Rate

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

Neural Networks vs. SVM SVM is faster Neural nets more accurate Percent Error Time (sec) Neural Networks 0% 16.4688 SVM 5% 0.0156 SVM is faster Neural nets more accurate Convolutional neural nets better at image recognition

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

References Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical Report 149300, June 2010. Suarez, Jesus. "Intro to Neural Networks." YouTube. N.p., 14 Dec. 2009. Web. 27 May 2015. Anguelov, Bobby. "Basic Neural Network Tutorial - Theory." Web log post. N.p., 3 Apr. 2008. 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. 35.1 (2013): 221-31. 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. 2015. Web. 28 May 2015. Dundar, Aysegul. "Convolutional Neural Networks." YouTube. N.p., 13 Jan. 2013. Web. 28 May 2015.

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