IDEA Image Display, Enhancement, and Analysis Department of Radiology and BRIC, UNC-Chapel Hill LINKS: L earning-based multi-source I ntegratioN frameworK for S egmentation of infant brain images Li Wang, Yaozong Gao, Feng Shi, Gang Li, Dinggang Shen Presented by Li Wang 09-18-2014
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Department of Radiology and BRIC, UNC-Chapel Hill LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images Li Wang,
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IDEA Image Display, Enhancement, and Analysis
Department of Radiology and BRIC, UNC-Chapel Hill
LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images
Li Wang, Yaozong Gao, Feng Shi, Gang Li, Dinggang ShenPresented by Li Wang
Limitations of multi-atlas label fusion1. nonlinear registrations2. simple intensity patch3. equal weight for different modality
Fractional anisotropy (FA) was calculated from Diffusion MRIs.
Our proposed work will 1. linear registrations2. appearance features and context features3. adaptive weights for different modality
2-weeks
6-months
12-months
T1 T2 FA Manual segmentation
Department of Radiology and BRIC, UNC-Chapel Hill
Flowchart of our proposed work
Context features
Appearance features
Classifier 2
Ground truth
T1 T2 FAAppearance
features
Probability mapsSequence classifier
Feature vectors
Context features
Appearance features
Classifier τ
Haar-like featuresClassifier 1
Random forests
Department of Radiology and BRIC, UNC-Chapel Hill
Result of an unseen target subject
T1 T2 FA
Original images
Iteration 1
Iteration 2
Iteration 10
Ground truth
Department of Radiology and BRIC, UNC-Chapel Hill
Probabilities of training image by the random forest
Post-processing: Anatomical constraint
To deal with the possible artifacts due to independent voxel-wise classification, we use patch-based sparse representation to impose an anatomical constraint [1] into the segmentation.
1. Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D., 2014. Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89, 152-164.
Ground truth of training images
Probabilities of target image by the random forest
𝛼1 𝛼 𝑖
Without anatomical With anatomical Ground truth
Department of Radiology and BRIC, UNC-Chapel Hill
Dataset
Dataset 1: UNC 119 infants consisting of 26, 22, 22, 23, and 26 subjects at 0-, 3-, 6-, 9- and 12-months of age, respectively.
Dataset 2: NeoBrainS12 MICCAI2012 Challenge. Dataset 3: SATA MICCAI2013 Challenge.
1. Coupé, P., Manjón, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.L., 2011. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940-954.
2. Zikic, D., Glocker, B., Criminisi, A., 2013. Atlas Encoding by Randomized Forests for Efficient Label Propagation. MICCAI 2013, pp. 66-73.3. Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D., 2014. Integration of sparse multi-modality representation and anatomical
2 training images with the manual segmentations. 3 target images for testing.
Department of Radiology and BRIC, UNC-Chapel Hill
Our results of 3 target images
Department of Radiology and BRIC, UNC-Chapel Hill
Quantitative measurement
Table 1. Dice ratios (DC) and modified Hausdorff distance (MHD) of different methods on NeoBrainS12 MICCAI Challenge data. (Bold indicates the best performance)
WM CGM BGT BS CB CSF
Placed Team Name DC MHD DC MHD DC MHD DC MHD DC MHD DC MHD UNC-IDEA 0.92 0.35 0.86 0.47 0.92 0.47 0.83 0.9 0.92 0.5 0.79 1.18 1
We have presented a learning-based method (LINKS) to effectively integrate multi-source images and the tentatively estimated tissue probability maps for infant brain image segmentation.
Experimental results on 119 infant subjects and MICCAI grand challenge show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods.