This document describes a method for automated fetal brain segmentation from 2D MRI slices in order to perform motion correction. The method uses box detection algorithms like MSER and SIFT to detect the brain region in each slice. It then trains a random forest classifier on brain and non-brain patches to perform brain extraction. Finally, it uses a conditional random field for motion correction across slices to generate a 3D volume with less artifacts from fetal movement. The results showed the proposed method produced motion-corrected volumes of diagnostic quality in 85% of test cases.

1. Automated Fetal Brain Segmentation from 2D
MRI Slices for Motion Correction
Kevin Keraudren, Maria Murgasova, Vanessa Kyriakopoulou,
Christina Malamateniou, Mary Rutherford, Bernhard Kainz,
Jo Hajnal & Daniel Rueckert
October 7th, 2014

2. Imaging the fetus using MRI
As the fetuse moves
the data is acquired as stacks of misaligned 2D slices
2/16

3. 3/16

4. 4/16

5. Motion correction of fetal brain MRI
orthogonal stacks
of 2D slices
3D volume
5/16

6. Box detection
MSER Size filtering Bag-of-SIFT RANSAC
MSER regions
Filtering by size and Bag-of-Words
6/16

7. Box detection
Brain and non-brain patches are learnt from the detected box and its
MSER regions
7/16

8. Brain extraction
Random Forest classifier
and Conditional Random Field
8/16

9. Motion correction
Adapted from Kuklisova-Murgasova et al., 2012.
9/16

10. Motion correction
Conditional Random Field
10/16

11. Results
Automated
segmentation
A B C
Manual
segmentation
A 42 3 0
B 2 8 5
C 0 1 5
A. no evident artefacts from motion
B. minor artifacts but can still be used for diagnosis
C. cannot be used for clinical diagnosis
11/16

12. Results
“In 85% of the tested cases, our proposed method produced a motion
corrected volume of a relevant quality for clinical diagnosis.”
NeuroImage, 2014.
12/16

13. Results
Tighter mask, less artifacts
and brain already extracted
13/16

14. Code
14/16

15. Code
15/16

16. Thanks!
www.doc.ic.ac.uk/~kpk09
kevin-keraudren.blogspot.co.uk
16/16