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PhD viva - 11th November 2015

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Short presentation given during the viva.

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PhD viva - 11th November 2015

  1. 1. Automated Organ Localisation in Fetal Magnetic Resonance Imaging K. Keraudren Thesis viva Supervisors: Prof. D. Rueckert & Prof. J.V. Hajnal
  2. 2. 1) Introduction 2) Localising the brain of the fetus 3) Localising the body of the fetus 4) Conclusion
  3. 3. Introduction
  4. 4. Imaging the developing fetus with MRI 4
  5. 5. Fast MRI acquisition methods MRI data is acquired as stacks of 2D slices that freeze in-plane motion but form an incoherent 3D volume. 5
  6. 6. Retrospective motion correction Orthogonal stacks of misaligned 2D slices 3D volume Localising fetal organs can be used to initialise motion correction. B. Kainz et al., “Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices,” in IEEE Transactions on Medical Imaging, 2015. 6
  7. 7. Retrospective motion correction Orthogonal stacks of misaligned 2D slices 3D volume Localising fetal organs can be used to initialise motion correction. B. Kainz et al., “Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices,” in IEEE Transactions on Medical Imaging, 2015. 6
  8. 8. Challenges in localising fetal organs 1 Arbitrary orientation of the fetus 2 Variability of surrounding maternal tissues 3 Variability due to fetal growth 7
  9. 9. Automated organ localisation Image registration: Warp annotated templates to new image Machine learning: Learn an abstract model from annotated examples Implicitly model variability: age pose (articulated body) maternal tissues 8
  10. 10. Automated organ localisation Image registration: Warp annotated templates to new image Machine learning: Learn an abstract model from annotated examples Implicitly model variability: age pose (articulated body) maternal tissues 8
  11. 11. Localising the fetal brain
  12. 12. 10
  13. 13. 10
  14. 14. Contributions: brain detection (Chapter 4) Preselection of candidate brain regions with MSER detection Filtering by size based on gestational age OFDOFD BPDBPD Slice-by-slice approach robust to the presence of motion K. Keraudren et al., “Localisation of the Brain in Fetal MRI using Bundled SIFT Features,” in MICCAI, 2013 11
  15. 15. Localisation results for the fetal brain (Chapter 4) Size inferred from gestational age Median error: 5.7 mm Improved results compared to Ison et al. (2012): 10 mm reported median error 12
  16. 16. Contributions: brain segmentation (Chapter 5) Label propagation from selected MSER Brain segmentation integrated with motion correction Fully automated motion correction K. Keraudren et al., “Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction,” in NeuroImage, 2014 15
  17. 17. Segmentation results for the fetal brain (Chapter 5) Fully automated motion correction in 85% of cases. Place holder, place holder, place holder. 16
  18. 18. Segmentation results for the fetal brain (Chapter 5) Improved results compared with the method of Taleb et al. (2013): Dice score of 93% versus 80%. 16
  19. 19. Localising the body of the fetus
  20. 20. 18
  21. 21. 18
  22. 22. Localising the body of the fetus Brain largest organ, ellipsoidal shape Lungs & liver irregular shapes Motivates 3D approach despite motion corruption (only coarse localisation) 19
  23. 23. Contributions: body detection (Chapter 6) Size normalisation based on gestational age 24 weeks 30 weeks 38 weeks Sequential localisation of fetal organs Image features steered by the fetal anatomy K. Keraudren et al., “Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features,” in MICCAI, 2015 20 uu vv
  24. 24. Contributions: body detection (Chapter 6) Size normalisation based on gestational age 24 weeks 30 weeks 38 weeks Sequential localisation of fetal organs Image features steered by the fetal anatomy K. Keraudren et al., “Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features,” in MICCAI, 2015 20 uu vv
  25. 25. Localisation results for the fetal organs (Chapter 6) 24 weeks 29 weeks 37 weeks Coronal plane Sagittal plane Transverse plane In 90% of cases, heart center detected with less than 10 mm error 21
  26. 26. Localisation results for the fetal organs (Chapter 6) 24 weeks 29 weeks 37 weeks Coronal plane Sagittal plane Transverse plane Automated motion correction in 73% of cases 21
  27. 27. Example localisation results
  28. 28. Conclusion
  29. 29. Conclusion Automated localisation of fetal organs in MRI: Brain, heart, lungs & liver Training one model across all ages Account for the unknown orientation of the fetus First method for a fully automated localisation of fetal organs beyond the brain Segmentation results enable fully automated motion correction 25
  30. 30. Thanks! Source code and trained models: github.com/kevin-keraudren/fetus-detector

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