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These slides are associated with the following NeuroImage publication: Keraudren, K., Kuklisova-Murgasova, M., Kyriakopoulou, V., Malamateniou, C., Rutherford, M.A., Kainz, B., Hajnal, J.V., Rueckert, D., Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction, NeuroImage (2014), doi: 10.1016/j.neuroimage.2014.07.023 http://www.sciencedirect.com/science/article/pii/S1053811914005953 For more details on using an XKCD style for latex beamer, visit: http://kevin-keraudren.blogspot.co.uk/2014/03/xkcd-style-beamer-presentation-latex.html
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Automated segmentation and motioncorrection of the fetal brain
Kevin KeraudrenImperial College London
December 6th, 2013
Definitions
recall=Volume of correctly classified voxels
Ground truth volume of the brain
recall=TP
TP + FN
precision=Volume of correctly classified voxels
Detected volume of the brain
precision=TP
TP + FP
4/12
Idea
99%
91%
96%
93%
Box detection
RF/CRF segmentation
Enlarged segmentation
Final segmentation
0
20
40
60
80
100
recallprecision
5/12
Box detection
MSERSize
filteringBag-of-SIFT RANSAC
MSER regions Filtering by size andBag-of-Words
6/12
Motion correction
Acquisition ofsnapshot images
Automatedmasking
Volumeregistration
Slice to volumeregistration
Update slicessegmentation
Robust statisticsand superresolution
Reconstructedvolume
9/12
Acknowledgements
Collaborators:Maria Murgasova, Vanessa Kyriakopoulou,Christina Malamateniou, Mary Rutherford,Bernhard Kainz, Jo Hajnal & Daniel Rueckert
XKCD for Matplotlib, Latex and Tikz:Jake Vanderplas, Damon McDougall, JohannesBuchner, percusse
www.doc.ic.ac.uk/~kpk09/
12/12