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Brain Tumor Segmentation @ Seoul AI 20180526

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Slides from my presentation on using deep learning for brain tumor segmentation.

Publié dans : Ingénierie
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Brain Tumor Segmentation @ Seoul AI 20180526

  1. 1. BRAIN TUMOR SEGMENTATION
  2. 2. Lars Sjösund Senior AI Research Engineer Peltarion Vice Chairman - Stockholm AI Jens Sjölund PhD, Medical Imaging Linköping University / Elekta
  3. 3. 1. PROBLEM BACKGROUND 2. U-NET 3. TIRAMISU 4. HANDS ON DEMO
  4. 4. 14 million new cancer cases per year 2/5 diagnosed at some point in life Sources: http://www.who.int/mediacentre/factsheets/fs297/en/ https://seer.cancer.gov/statfacts/html/all.html
  5. 5. BRAIN TUMOR SEGMENTATION
  6. 6. PROBLEM SETUP 276 patients - 3D MRI scans 4 input channels - 4 tumor classes INPUT TARGET Flair T1 T1c T2 Tumor map Source: http://braintumorsegmentation.org/
  7. 7. Can be fed raw data, adapt, learn to interpret and conclude ARTIFICIAL NEURAL NETWORKS
  8. 8. AUTOENCODER INPUT OUTPUTENCODER DECODER
  9. 9. AUTOENCODER INPUT OUTPUTENCODER DECODER
  10. 10. U-NET INPUT OUTPUT
  11. 11. INPUT OUTPUT 3x3 Conv Dropout ReLU Pooling
  12. 12. INPUT OUTPUT 3x3 Conv Dropout ReLU Upsampling
  13. 13. Initial Results 97,6% pixel level accuracy!
  14. 14. TARGET PREDICTEDINPUT Initial Results
  15. 15. 2.4% 97.6% Class Balance No tumor Tumor
  16. 16. L = − pij log !pij j ∑ i ∑ ˆL = − 1 fj pij log !pij j ∑ i ∑ 2.4% 97.6% Class Balance No tumor Tumor
  17. 17. Weighted Loss Results TARGET PREDICTEDINPUT
  18. 18. Mistakes TARGET PREDICTEDINPUT
  19. 19. Results - Similarity scores Doctors U-Net with weighted loss Whole 85% (±8%) 82% (±16%) Core 75% (±24%) 71% (±21%) Enhancing 74% (±13%) 60% (±30%)
  20. 20. DenseNet / Tiramisu Source: https://arxiv.org/abs/1608.06993
  21. 21. DenseNet / Tiramisu Sources: https://arxiv.org/abs/1608.06993 https://arxiv.org/abs/1611.09326
  22. 22. Results - Similarity scores Doctors U-Net with weighted loss Tiramisu with weighted loss Tiramisu w/o weighted loss Whole 85% (±8%) 82% (±16%) 81%(±17%) 84%(±14%) Core 75% (±24%) 71% (±21%) 69%(±22%) 66%(±24%) Enhancing 74% (±13%) 60% (±30%) 60%(±31%) 61%(±31%)
  23. 23. #CONFIGS TRIED
 ~ 100 < 1000 
 LINES OF CODE
 
 ~20 FOR MODEL TIME PER CONFIG 
 < 13 HOURS TENSORFLOW, KERAS, TENSORBOARD
  24. 24. GPU 4 TITAN X 12 GB 256 GB RAM 40 CORES OF INTEL XEON E5-2630 COST
 100K SEK Models trained on in-house desktop
  25. 25. Demo time!
  26. 26. Thank you!
  27. 27. Lars Sjösund lars@peltarion.com @sjosund Questions? M A K I N G A I A V A I L A B L E F O R E V E R Y O N E

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