Slides from the reading group presentation where I introduced a new Python interface for IRTK. See http://kevin-keraudren.blogspot.co.uk/2013/12/irtk-python.html for more details and the iPython notebook demo.

1. Organ Detection in Fetal MRI
Kevin Keraudren
Imperial College London
May 22nd , 2013

2. 1
Overview
2
Python interface for IRTK
3
Future work
2/27

3. 1
Overview
2
Python interface for IRTK
3
Future work
3/27

4. Overview
1
Brain detection &
reconstruction
2
3D visualisation
3
Organ localisation
4/27

5. People
Brain reconstruction:
Maria Murgasova
3D visualisation:
Christina Malamateniou
Organ detection:
Bernhard Kainz
5/27

6. Localisation of the Brain in Fetal MRI Using
Bundled SIFT Features
For every slice
Detect MSER regions
Filter by size
RANSAC
Classify using SIFT features
6/27

7. Detections results averaged over the cross
validation (all orientations combined)
Centiles
25th
50th
75th
2D SIFT
10.9
15.5
20.5
Error (mm)
3D SIFT
14.8
20.8
30.4
Bundled SIFT
4.0
5.7
8.4
Detection
Complete brain
98%
38%
85%
23%
100%
85%
7/27

8. 1
Overview
2
Python interface for IRTK
3
Future work
8/27

9. Why Python?
ls *.nii | python -c ’import sys, irtk;
[sys.stdout.write( str(irtk.imread(line.rstrip(),
dtype="float32").max())+"n")
for line in sys.stdin]’
numpy, matplotlib, OpenCV, VTK, scipy.ndimage...
& C++ via cython
9/27

10. Why another library?
import itk
pixelType = itk.UC
imageType = itk.Image[pixelType, 3]
readerType = itk.ImageFileReader[imageType]
writerType = itk.ImageFileWriter[imageType]
reader = readerType.New()
reader.SetFileName( "input.nii" )
reader.Update()
itk2np = itk.PyBuffer[imageType]
data = itk2np.GetArrayFromImage( reader.GetOutput() )
...
10/27

11. Why another library?
import SimpleITK as sitk
img = sitk.ReadImage( "input.nii" )
data = sitk.GetArrayFromImage( img )
...
output = sitk.GetImageFromArray( data )
output.SetSpacing( img.GetSpacing() )
output.SetOrigin( img.GetOrigin() )
output.SetDirection( img.GetDirection() )
sitk.WriteImage( output, "output.nii" )
11/27

12. Why another library?
import irtk
img = irtk.imread( "input.nii" )
...
irtk.imwrite( "output.nii", img )
12/27

13. Key features
simple and pythonic
overload getitem instead of GetRegion
parallelisation using joblib
visualisation functions
13/27

14. Implementation
template <class dtype>
void irtk2py( irtkGenericImage<dtype>& irtk_image,
dtype* img,
double* pixelSize,
double* xAxis,
double* yAxis,
double* zAxis,
double* origin,
int* dim );
14/27

15. Implementation
void py2rigid( irtkRigidTransformation &transform,
double tx,
double ty,
double tz,
double rx,
double ry,
double rz,
bool invert=false );
15/27

16. Demo
ipython notebook
16/27

17. Parallelisation example
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import irtk
from joblib import Parallel, delayed
from glob import glob
def register( f, img1):
img2 = irtk.imread( f, dtype=’float32’)
img2 = img2.rescale().resample(2)
t = img2.register(img1)
return t
filenames = glob( raw_folder + "/" + patient_id + "_*.nii")
img1 = irtk.imread(filenames[0], dtype=’float32’)
img1 = img1.rescale().resample(2)
transformations = [irtk.RigidTransformation()]
transformations.extend( Parallel(n_jobs=-1)(delayed(register)(f,
img1)
for f in filenames[1:] )
)
17/27

18. 1
Overview
2
Python interface for IRTK
3
Future work
18/27

19. Coarse segmentation as side product of detection
19/27

20. Convex hull of all detections
20/27

21. Iterative detection/reconstruction
21/27

22. Iterative detection/reconstruction
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23. Iterative detection/reconstruction
23/27

24. Room for improvement
Refine motion model (statistics on slice transformations)
Patch based segmentation instead of graphcut
Detect head orientation to solve more difficult registrations
Could the detection process produce
more than a mask?
24/27

25. Could we reconstruct more than the brain?
25/27

26. For the future (hanging projects)
Spine detection using SLIC supervoxels
Dense volumetric features: SURF 3D
Alignment of mothers’ bodies to model of a pregnant woman
26/27

27. Suggestions or questions?
27/27