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Automated Organ Localisationin Fetal Magnetic Resonance Imaging
K. Keraudren
Thesis viva
Supervisors: Prof. D. Rueckert & Prof. J.V. Hajnal
1) Introduction
2) Localising the brain of the fetus
3) Localising the body of the fetus
4) Conclusion
Introduction
Imaging the developing fetus with MRI
4
Fast MRI acquisition methods
MRI data is acquired as stacks of 2D slicesthat freeze in-plane motion
but form an incoherent 3D volume.
5
Retrospective motion correction
Orthogonal stacks ofmisaligned 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
Retrospective motion correction
Orthogonal stacks ofmisaligned 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
Challenges in localising fetal organs
1 Arbitrary orientation of the fetus
2 Variability of surrounding maternal tissues
3 Variability due to fetal growth7
Automated organ localisation
Image registration:I Warp annotated templates to new image
Machine learning:I Learn an abstract model from annotated examplesI Implicitly model variability:
F ageF pose (articulated body)F maternal tissues
8
Automated organ localisation
Image registration:I Warp annotated templates to new image
Machine learning:I Learn an abstract model from annotated examplesI Implicitly model variability:
F ageF pose (articulated body)F maternal tissues
8
Localising the fetal brain
10
10
Contributions: brain detection (Chapter 4)
Preselection of candidate brain regions with MSER detection
Filtering by size based on gestational age OFDOFDBPDBPD
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
Localisation results for the fetal brain (Chapter 4)
Size inferred from gestational ageMedian error: 5.7 mm
Improved results compared to Ison et al. (2012):10 mm reported median error
12
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
Segmentation results for the fetal brain (Chapter 5)
Fully automated motion correction in 85% of cases.Place holder, place holder, place holder.
16
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
Localising the body of the fetus
18
18
Localising the body of the fetus
Brain largest organ, ellipsoidal shapeLungs & liver irregular shapes
Motivates 3D approach despite motion corruption(only coarse localisation)
19
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 withSteerable Features, in MICCAI, 2015
20
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 withSteerable Features, in MICCAI, 2015
20
Localisation results for the fetal organs (Chapter 6)
24 weeks
29 weeks
37 weeks
Coronal plane Sagittal plane Transverse planeIn 90% of cases, heart center detected with less than 10 mm error 21
Localisation results for the fetal organs (Chapter 6)
24 weeks
29 weeks
37 weeks
Coronal plane Sagittal plane Transverse planeAutomated motion correction in 73% of cases 21
Example localisation results
Conclusion
Conclusion
Automated localisation of fetal organs in MRI:Brain, heart, lungs & liverTraining one model across all agesAccount for the unknown orientation of the fetusFirst method for a fully automated localisation of fetal organsbeyond the brainSegmentation results enable fully automated motion correction
25
Thanks!Source code and trained models:
github.com/kevin-keraudren/fetus-detector
http://github.com/kevin-keraudren/fetus-detector/
IntroductionLocalising the fetal brainLocalising the body of the fetusConclusion
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