1. Li Wang, Yaozong Gao, Feng Shi, Gang Li, Dinggang Shen
LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images
Li Wang, Yaozong Gao, Feng Shi, Gang Li, Dinggang Shen
Presented by Li Wang

2. Content
Motivation
Proposed method
Experimental results
Conclusion

3. Motivation 2-weeks 6-months 12-monthsFA
Manual segmentation
Fractional anisotropy (FA) was calculated from Diffusion MRIs.
2-weeks
6-months
12-months
It is difficult to do the segmentation for the infant image due to the low resolution, high imaging noise and especially the dynamical brain development. Based on the development, the infant brain usually divided into 3 stages: isfantile, isoietense and early adult-like stagges. We can take T1 modality for example, we can see the intensity of WM is increasing while the GM is decreasing, therefor at around 6 month, the intensities of WM and GM are the same, which results in extremely low contrast. To better segment the 6-month image, we may borrow the guidance from FA image, from which we can still find some clues to identify the WM/GM boundaries.
most of previous methods employ multi-atlas label fusion, they employ using T2, T2 and FA images.
Limitations of multi-atlas label fusion
nonlinear registrations
simple intensity patch
equal weight for different modality
Our proposed work will
linear registrations
appearance features and context features
adaptive weights for different modality

4. Flowchart of our proposed work
Classifier 1
Random
forests
Ground
truth T1 T2 FA
Appearance features
Appearance features
Classifier 2
Haar-like features
Context features
Sequence classifier
Feature vectors
Context features
Appearance features
Classifier τ
Probability maps

5. Result of an unseen target subject
Original images
Iteration 1
Iteration 2
Iteration 10
Ground truth

6. 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.
Without anatomical
With anatomical
Ground truth
Ground truth of training images
Probabilities of training image by the random forest
𝛼 1
𝛼 𝑖
Probabilities of target image by the random forest
1. Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D., Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89,

7. 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.

8. Importance of the context features
Iterations

9. Importance of the multi-source

10. Dataset 1: UNC 119 infants Majority voting (MV)
Nonlocal label fusion [1]
Atlas forest [2]
Patch-based sparse labeling [3]
Proposed1 (Random forest)
Proposed2 (Random forest + Anatomical constraint)
Coupé, P., Manjón, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.L., Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54,
Zikic, D., Glocker, B., Criminisi, A., Atlas Encoding by Randomized Forests for Efficient Label Propagation. MICCAI 2013, pp
Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D., Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89,

11. Slice comparisons Segmentation Difference maps with the ground truthFA
Ground truth
Segmentation
Difference maps with the ground truth
(b) Nonlocal
label fusion
(d) Patch-based
sparse labeling
(a) Majority voting
(c) Atlas forest
(e) Proposed1
(f) Proposed2

12. Inner surface comparisons
(b) Nonlocal
label fusion
(d) Patch-based
sparse labeling
(a) Majority voting
(c) Atlas forest
(e) Proposed1
(f) Proposed2
(g) Ground truth

13. Quantitative measurement

14. Dataset 2: NeobrainS12 MICCAI Challenge
2 training images with the manual segmentations.
3 target images for testing.

15. Our results of 3 target images

16. 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)

17. Dataset 3: SATA MICCAI2013 Challenge
35 training images with the 14 ROIs in subcortical regions.
12 target images for testing.

18. Our results on one target image

19. Quantitative measurement
Table 2. Dice ratios (DC) and Hausdorff distance (HD) of different methods on SATA MICCAI Challenge data.

20. Conclusion
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.

21. Thanks for your attention!
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