This document describes a statistical segmentation method for automatically segmenting normal appearing white matter lesions in multiple sclerosis patients using multimodal MRI. The method uses a statistical segmentation approach combining T1-weighted, T2-FLAIR and diffusion tensor imaging scans with a healthy control DTI atlas and multiple sclerosis lesion probability prior to segment lesions. Evaluation on 15 multiple sclerosis patients found the automatic segmentation method achieved a DICE similarity coefficient of 0.67 and area under the ROC curve of 0.81 when compared to manual segmentations, demonstrating the potential of the approach.

1. Multimodal MRI
statistical segmentation
of normal appearing
white matter lesions in
multiple sclerosis
Antonio Carlos da S. Senra Filho, Antonio Carlos dos Santos, Luiz Otávio Murta Junior
Department of Computing and Mathematics
Medicine School of Ribeirao Preto
Institute of Psychiatry, Psychology and Neuroscience
University of Sao Paulo, Brazil
King’s College London, UK

2. Brief introduction
Multiple Sclerosis
● Neurodegenerative disease (CNS)
● Inflammatory process
● White matter and spinal cord
● RRMS, SPMS, PPMS, PRMS
Etiology:
● Young adults (~30-40)
● ⅔ women
● Hereditary, environmental factors?
Adapted from [1]

3. Adapted from [2]

4. Adapted from [2]

5. MRI in MS
MRI:
● McDonald criteria [5]
● T1, T1-Gd, PD, T2, T2-FLAIR
● Diffusion weighted images
Adapted from [3]
Adapted from [4]

6. MRI in MS
● McDonald criteria [5]
● T1, T1-Gd, PD, T2, T2-FLAIR
● Diffusion weighted images
● NAWM
Adapted from [3]
Adapted from [4]

7. Automatic Approaches
Many efforts have been made to
automatic lesion segmentation
Adapted from [3,6]

8. Step further
Apply high
dimensional
segmentation (T1,
T2-FLAIR and DTI)
DTI scalar maps
showed higher
sensitivity to NAWM
Recently only group
analysis have been
made...what about
patient specific?
Adapted from [7]

9. Methods
Statistical Segmentation
DTI template: 131 healthy subjects, 35-45 years old, 72 women, affine+diffeomorphic
registration, scalar maps in 1mm/2mm (FA,RA,MD and RD)
MS prior: 52 SPMS, 30-55 years old, manually segmented
DTI reconstruction: 32 gradient direction, 72 axial slices, FOV = 256 x 256 mm, matrix
size of 128x128, 2x2x2 isotropic voxel resolution, TR/TE = 8391/65 ms e b-factor = 1000
mm/s2, eddy correction (FSL-EDDY), weighted least-square reconstruction, scalar DTI
maps (FA, MD, RA, RD)

10. Methods
Patient Analysis
DTI data: 15 patients DTI scalar maps (FA,RA,MD and RD), manual segmentation from
experienced radiologist
T1: pulse gradient-echo, TR /TE = 970/4 ms, flip angle of 12°, matrix size of 256 x 256 mm,
FOV = 256 mm, 1x1x1 mm voxel resolution
T2-FLAIR: TR/TE/TI = 9000/144/2500 ms, matrix size of 256 x 256 mm, FOV = 256 mm,
1x1x1 mm voxel resolution
DTI: 32 gradient direction, 72 axial slices, FOV = 256 x 256 mm, matrix size of 128x128,
2x2x2 isotropic voxel resolution, TR/TE = 8391/65 ms e b-factor = 1000 mm/s2, eddy
correction (FSL-EDDY), weighted least-square reconstruction, scalar DTI maps (FA, MD,
RA, RD)

11. Methods

12. Methods
Logistic Regression Image Enhancement
(𝛙 ￫ Otsu threshold)
Bayesian Segmentation
Morphological Shape Constraints [8]

13. Results
DTI Atlas reconstruction
Affine+Diffeomorphic ICBM space
normalization (131 healthy
subjects)
FA, MD, RA, and RD volumes in
1mm/2mm [9]
Baseline for statistical residual
comparison

14. Results
MS Lesion Prior Probability
Affine+Diffeomorphic ICBM space
normalization (52 SPMS patients)
Lesion volumes in 1mm/2mm
Baseline for statistical Bayesian
statistical segmentation

15. patient FA in ICBM space histogram matching residual image (Atlas - Patient)
Logistic Enhancement NAWM map

16. T1w T2w-FLAIR

17. T1w T2w-FLAIR
hyperintense
lesions
hypointense lesions

20. Results
Agreement with expert evaluation
Metric evaluation mean value (standard deviation)
Sensitivity 0.76
Area under ROC curve 0.81
DICE 0.67 (0.11)
Volume Similarity 0.75 (0.20)
Absolute Lesion Load Difference 5.42 (3.74)

21. Conclusion
DTI scalar maps increase the sensitivity to NAWM lesions segmentation
Automatic pipeline (T1w, T2w-FLAIR and DTI scalar maps) showed promising results for a
broad range of MS lesions types (hypointense, hyperintense and NAWM)
Although time consuming (~40 minutes), the automatic approach still offer a reasonable
solution for MS lesion segmentation

22. References
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