MRI Segmentation. MRI Bootcamp, 14 th of January J. Miguel Valverde
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1 MRI Segmentation MRI Bootcamp, 14 th of January 2019
2 Segmentation
3 Segmentation
4 Information Segmentation Algorithms Approach
5 Types of Information Local
6 Types of Information Local Contextual.
7 Types of Information Local Contextual. Spatial Y axis X axis
8 Approaches classification Atlas registration based Hand-crafted features (Machine Learning) Learnt features (Deep Learning) Chen H. et al. NeuroImage 2018.
9 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Overview Image Graph Cuts Level Sets Non-linear Registration min E(s) Segmentation
10 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Overview Image Atlases Graph Cuts Level Sets Non-linear Registration T1, T2, PD min E(s) Segmentation
11 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Overview Image Atlases Graph Cuts Level Sets Non-linear Registration Initial Estimation T1, T2, PD min E(s) Segmentation
12 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar
13 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Not realistic Sensitive to outliers
14 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Biased
15 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Multi-atlas approach N most similar Use many as single-atlas
16 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Majority voting. Multi-atlas approach N most similar Use many as single-atlas Majority voting (Artaechevarria X. et al. Trans. Med. Im ) Y v = arg max f(a i v, y) i=0 y where f A i v, y = 1 if Ai v = y i 0 if A v y N Y v final label at voxel v, A v i atlas A i at voxel v, N number of atlases y label
17 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Multi-atlas approach N most similar Use many as single-atlas Bayesian statistics P Y X = where X voxel Y label P X Y P(Y) P(X) Majority voting (Artaechevarria X. et al. Trans. Med. Im ) Bayes (Ali AA. et al. NeuroImage 2005.)
18 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Single-atlas approach Average Most similar Multi-atlas approach N most similar Use many as single-atlas Markov Random Field P y v y S v = P(y v y Nv ) where v voxel S set of all voxels N v neighbor voxels of v Majority voting (Artaechevarria X. et al. Trans. Med. Im ) Bayes (Ali AA. et al. NeuroImage 2005.) MRF (Bae MH. et al. NeuroImage 2009.)
19 Approaches: Atlas registration based Hand-crafted features Learnt features Atlas registration based Disadvantages: Registration is needed (affine and/or non-linear). Computationally expensive. Very sensitive to registration.
20 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) # 1. Generate a feature collection for voxel in allvoxels: f = generatefeaturevector(voxel) allfeatures.append(f)
21 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) # 1. Generate a feature collection for voxel in allvoxels: f = generatefeaturevector(voxel) allfeatures.append(f) # 2. Train a model model = SVM(parameters) model.fit(allfeatures)
22 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) Vector of features: Local intensities (Wu T. et al. NeuroImage 2012.).
23 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) Vector of features: Local intensities (Wu T. et al. NeuroImage 2012.) Neighbour intensities, gradients (Pereira S. et al. Journal of Neuroscience Methods 2016).
24 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) Vector of features: Local intensities (Wu T. et al. NeuroImage 2012.) Neighbour intensities, gradients (Pereira S. et al. Journal of Neuroscience Methods 2016) Multi-scale (Bae MH. et al. NeuroImage 2009.)..
25 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) Vector of features: Local intensities (Wu T. et al. NeuroImage 2012.) Neighbour intensities, gradients (Pereira S. et al. Journal of Neuroscience Methods 2016) Multi-scale (Bae MH. et al. NeuroImage 2009.). Coordinates (Wachinger C. et al. IEEE Trans. Biomed. Eng ) Requires Registration! x, y, z
26 Approaches: Atlas registration based Hand-crafted features Learnt features Hand-crafted features (Machine Learning) Advantages No registration needed (in principle). Rotational invariant Understanding of the features. Can easily try different ML algorithms.
27 Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Feature Vector ML 0 1 GM WM T1 intensity 0 CSF T2 intensity Neighbor gradients
28 Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Feature Vector ML 0 1 GM WM T1 intensity 0 CSF T2 intensity Neighbor gradients Neural Network GM WM CSF
29 Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Convolutions NxN Image Kernel (N-2)x(N-2) =
30 GM WM CSF Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Neural Network 3x3 3x3 5x5 3x3 = = 1x softmax Output ( y) Labels (y)
31 GM WM CSF Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Neural Network 3x3 3x3 5x5 3x3 = = e 1x softmax Output ( y) H( y, y) Cross Entropy Labels (y)
32 Learnt features (Deep Learning) Types of information/data Convolutions (region) Local + Contextual
33 Learnt features (Deep Learning) Types of information/data Convolutions (region) Convolutions (full) Local + Contextual + Spatial
34 Learnt features (Deep Learning) Types of information/data Spatial Convolutions (region) Convolutions (full) Other information Distance to centroids (de Brebisson A. and Montana G. IEEE CVPR Workshop 2015.)
35 Learnt features (Deep Learning) Types of information/data Convolutions (region) Convolutions (full) Other information Spatial Distance to centroids (de Brebisson A. and Montana G. IEEE CVPR Workshop 2015.) Encoded spatial information (Rachmadi MF. et al. Compu. Med. Imaging Graph 2018.)
36 Approaches: Atlas registration based Hand-crafted features Learnt features Learnt features (Deep Learning) Advantages No feature engineering needed. Extrapolate to other tasks. Parallel processing capabilities. Disadvantages Black box
37 Pre-processing operations Denoising
38 Pre-processing operations Denoising Inhomogeneity correction Intensity inhomogeneity in MR brain image. (Vovk U. et al. IEEE Trans. Med. Imag. 2007)
39 Pre-processing operations Denoising Inhomogeneity correction 0 mean, 1 variance
40 Pre-processing operations Denoising Inhomogeneity correction 0 mean, 1 variance Data augmentation Rotation
41 Pre-processing operations Denoising Inhomogeneity correction 0 mean, 1 variance Data augmentation Rotation Non-linear transformations
42 Conclusion / Take home message Important to understand what we have and what we want. Check the data. Then, check the data again.
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