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

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

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

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.

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.

Pre-processing: Grey Matter Threshold

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. 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

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. 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

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. 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pages 1–2, 2015.

Classification: Random Forests

Classification: Feature Importance

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

Post-processing: Markov Random Field Dice Score

Results with training subjects