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

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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

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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

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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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