Li Wang, Yaozong Gao, Feng Shi, Gang Li, Dinggang Shen Presented by Li Wang 09-18-2014

1. 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 09-18-2014

2. Content • Motivation • Proposed method • Experimental results • Conclusion

3. Motivation Manual segmentation T2 T1 FA Fractional anisotropy (FA) was calculated from Diffusion MRIs. 2-weeks 6-months 12-months 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 T1 T2 FA Appearance features Ground truth Classifier 1 Random forests Context features Appearance features Haar-like features Classifier 2 Context features Appearance features Feature vectors • Classifier τ Sequence classifier Probability maps

5. Result of an unseen target subject T1 T2 FA 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. Ground truth of training images Probabilities of training image by the random forest Probabilities of target image by the random forest With anatomical Ground truth Without anatomical 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.

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 Iterations 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., 2011. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940-954. Zikic, D., Glocker, B., Criminisi, A., 2013. Atlas Encoding by Randomized Forests for Efficient Label Propagation. MICCAI 2013, pp. 66-73. 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.

11. Slice comparisons T1 T2 FA Ground truth Segmentation Difference maps with the ground truth (d) Patch-based sparse labeling (b) Nonlocal label fusion (a) Majority voting (c) Atlas forest (e) Proposed1 (f) Proposed2

12. Inner surface comparisons (d) Patch-based sparse labeling (b) Nonlocal label fusion (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) http://neobrains12.isi.uu.nl/mainResults_Set1.php

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 Hausdorffdistance (HD) of different methods on SATA MICCAI Challenge data. http://masi.vuse.vanderbilt.edu/submission/leaderboard.html

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! http://www.unc.edu/~liwa/ Google scholar