Algorithms for medical image registration and segmentation
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1 Algorithms for medical image registration and segmentation Multi-atlas methods
2 Overview Medical imaging hands-on Data formats: DICOM, NifTI Software: OsiriX, Slicer, ITK, Convert3D Image Registration Deformation Models Software: ElastiX Multi-Atlas Segmentation Joint Label Fusion Software: PICSL MALF Example Application: Label Neonatal Brains on MR
3 Medical Image Data DICOM: Digital Imaging and Communication in Medicine Standard in clinical applications Very good for data/patient management Very bad for anything else first step: transform your data
4 Medical Image Data NIfTI: Neuroimaging Informatics Technology Initiative Much more compact file format Retains information about patient orientation Removes everything else
5 (Open Source) Software DICOM viewers OsiriX (mac only )
6 (Open Source) Software DICOM viewers OsiriX (mac only )
7 (Open Source) Software DICOM viewers OsiriX (mac only )
8 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform)
9 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform)
10 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform)
11 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform) Toolkits ITK: MIRTK:
12 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform) Toolkits ITK: MIRTK: The Swiss army knife ITK Snap & Convert3D:
13 (Open Source) Software DICOM viewers OsiriX (mac only ) Slicer (cross-platform) Toolkits ITK: MIRTK: The Swiss army knife ITK Snap & Convert3D:
14 Image Registration The task of aligning two images or volumes different modalities different time-points different patients
15 [Sotiras 2013] Image Registration? Source Target
16 [Sotiras 2013] Image Registration W Source Target
17 [Sotiras 2013] Image Registration S W T
18 [Sotiras 2013] Image Registration S W T
19 [Sotiras 2013] Image Registration S W T arg min E(W )=M(S W, T )+R(W ) W
20 [Sotiras 2013] Image Registration S W T arg min E(W )=M(S W, T )+R(W ) W matching term regularisation term
21 [Sotiras 2013] Image Registration S W T arg min E(W W )=M(S W, W T )+R(W W) W
22 [Sotiras 2013] Image Registration sx 0 0 tx 0 sy 0 ty 0 0 sz tz W Rigid: rotation + scaling
23 Image Registration sx.. tx. sy. ty.. sz tz... 1 W Affine: rotation + scaling + shearing
24 Image Registration W Non-rigid
25 Image Registration W non-diffeomorphic
26 [Sotiras 2013] Image Registration S W T arg min E(W )=M(S W, T )+R(W R ) W
27 Image Registration with ElastiX implements rigid, affine and important non-rigid algorithms 2-4D easy configuration database of configuration files for different applications: Parameter_file_database
28 Atlas-based Segmentation Atlas : a volume with a manual annotation of the structure of interest Atlas-based segmentation: use registration to map annotation onto new case & use for segmentation MALF: Multi Atlas Label Fusion
29 Atlas-based Segmentation Main questions: Which atlases to use? How to combine labels?
30 Atlas selection in MALF Take nearest neighbours Intensities distance Registration distance external knowledge
31 Atlas selection in MALF Take nearest neighbours Intensities distance Registration distance external knowledge Learning in atlas space clustering distribution shape; manifold
32 Label fusion in MALF Average selected atlases Weighted average globally locally Weighted average + statistics
33 Idea: We can learn many things about our atlases Most importantly: their correlation with image intensities with each other
34 A1 = (F1, S1 ) Given n matched atlases How to determine target segmentation? S T =? An = (Fn, Sn )
35 A1 = (F1, S1 ) Majority vote at each position x S T (x) = argmax l 2 {1... L} l Si (x) = n X Sil (x) i=1 1 if Si (x) = l 0 otherwise An = (Fn, Sn )
36 A1 = (F1, S1 ) Weighted vote at each position x S T (x) = n X i=1 n X i=1 wi (x)sil (x) wi (x) = 1 An = (Fn, Sn )
37 A1 = (F1, S1 ) Weighted vote at each position x S T (x) = n X wi (x)sil (x) i=1 1 wi (x) = e Z(x) P 2 [F (y) F (y)] / i T y2n (x) An = (Fn, Sn )
38 A1 = (F1, S1 ) Formulate as statistical learning problem Consider possibly correlated labeling errors An = (Fn, Sn )
39 ST (x) = Si (X) + i (x) label difference i (x) 2 { 1, 0} when Si (x) = 1 i (x) 2 {0, 1} when Si (x) = 0 note: this formulation considers only two labels - but can easily be extended argmin E FT X n i=1 i wi (x) (x) 2 FT, F1,... Fn
40 argmin E F T apple n X i=1 w i (x) i (x) 2 F T,F 1,...F n = nx i=1 nx j=1 w i (x)w j (x)e[ i (x) j (x) F T,F 1...F n ] = w > x M x w x w x = M 1 estimates how likely two atlases both produce wrong segmentation closed form solution x 1 n 1 > n Mx 1 1 n
41 M x (i, j) / w > x (M x + I)w subject to apple find w by minimizing nx i=1 find M from image intensities X y2n(x) w x (i) =1 F T (y) F i (Y ) F T (y) F j (y)
42
43 Label Voting with PICSL MALF picsl_malf/
44 References A. Sotiras, C. Davatzikos, N. Paragios, Deformable medical image registration: a survey, IEEE Transactions on Medical Imaging 32 (7), (2013), J. E. Iglesias, M. R. Sabuncu, Multi-atlas segmentation of biomedical images: a survey. Medical image analysis, 24(1), (2015), H. Wang, J. W. Suh, S. R. Das, J. B. Pluta, C. Craige, P. A. Yushkevich, Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), (2013),
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