1. Random Forests For Multiple Sclerosis Lesion Segmentation
F.J Vera Olmos , H. Melero , N. Malpica

2. Pipeline Pre-processing Features Classification Post-processing
Tissue segmentation
Intensity standardization
GM threshold
Features
Intensity
Tissue
Distance
Classification
Random Forests
Post-processing
Markov Random Field
Lesion growing

3. Pre-processing: Tissue Segmentation
Tissue segmentation with SPM using T1. WM GM
CSF

4. Pre-processing: Intensity Standardization
Laszlo G Nyul, Jayaram K Udupa, and Xuan Zhang. New variants of a method of mri scale standardization. IEEE transactions on medical imaging, 19(2):143–150, 2000.

5. Pre-processing: Grey Matter Threshold
GM should be the brightest tissue in FLAIR
µ - Mean intensity of GM
θ - Full Width at Half Maximum
Eloy Roura, Arnau Oliver, Mariano Cabezas, Sergi Valverde, Deborah Pareto, Joan C Vilanova, Lluís Ramio-Torrentà, Àlex Rovira, and Xavier Lladó. A toolbox for multiple sclerosis lesion segmentation. Neuroradiology, 57(10):1031–1043, 2015.

6. Pre-processing: Grey Matter Threshold

7. Features: Intensity Based Features
Voxel intensity
Voxel intensity value after smoothing with Gaussian filter at σ = 3, 5 & 7 mm
Voxel difference between its neighborhood at 3, 5 & 7 mm
Oskar Maier and Heinz Handels. Ms lesion segmentation in mri with random forests. Proc Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

8. Features: Tissue Based Features
Partial volume obtained from the tissue segmentation
Partial volume after smoothing with Gaussian filter at σ = 3, 7 & 15 mm
Oskar Maier and Heinz Handels. Ms lesion segmentation in mri with random forests. Proc Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

9. Features: Distance Based Features
Distance to the center of the brain
Distance to the external CSF
Oskar Maier and Heinz Handels. Ms lesion segmentation in mri with random forests. Proc Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

10. Classification: Random Forests

11. Classification: Feature Importance

12. Post-processing: Markov Random Field
RF generates a probability mask
Initial lesion mask generated using a threshold θ
Using FLAIR intensities
Gamma distribution trained with initial lesion mask
Mixture of three Gaussian trained using the remaining voxels

13. Post-processing: Markov Random Field
Dice Score

14. Results with training subjects