automated detection of wm and cortical lesions in early ... · 2015-11-24, fartaria m.j. page 29...

Swiss Medical Image Computing Day siemens.ch/answers

Automated Detection of WM and Cortical

Lesions in Early-Stages of MS

Mário João Fartaria

Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG, Lausanne, Switzerland.

2015-11-24, Fartaria M.J.

Swiss Medical Image Computing Day

Outline

Multiple Sclerosis

Introduction

Methodology and Results

Conclusions

Future Trends



Multiple Sclerosis

Myelin degeneration

of the brain



Multiple Sclerosis

Non-pathological

tissue Myelin degeneration

of the brain



Multiple Sclerosis

Nerve Cell

Non-pathological


of the brain



Multiple Sclerosis

Nerve Cell

Non-pathological


of the brain

Myelin sheath Nerve fiber



Multiple Sclerosis

Nerve Cell

Non-pathological


of the brain


Exposed

nerve fiber

Damaged

myelin sheath



Multiple Sclerosis

Nerve Cell

Non-pathological


of the brain


Exposed

nerve fiber

Damaged

myelin sheath

MS

LESION



Outline

Multiple Sclerosis

Introduction


Conclusions

Future Trends



MRI



MRI – Brain Tissues

Cerebrospinal Fluid Grey Matter WM Matter

MPRAGE



MRI - Contrasts

MPRAGE FLAIR

DIR MP2RAGE T1map1) MP2RAGE Uniform1)

1)Marques et al. NeuroImage. 2010



MRI in Multiple Sclerosis


FLAIR



MRI in Multiple Sclerosis


FLAIR MS Lesions



MRI – MS Lesions

FLAIR MS Lesions

WM lesions

(pure WM)

Cortical Type I

(across WM/GM)

Cortical Type II

(within the cortex)

Cortical Type III

(sub-pial lesions

over one gyrus)

White Matter Cortex



Manual Segmentation - Limitations

Lesions with poor contrast

Time-consuming

Intra-observer variability

Inter-observer variability



State-of-the-Art

0

2

4

6

8

10

12

14

16

N.°

of

S

tud

ies

Year of Publication

Automated methods show less agreement with manually segmented scans

than those obtained with segmentations by independent observers2)

2)Garcia-Lorenzo et al. Med image Anal. 2013



State-of-the-Art3)

0

5

10

15

20

25

30

35

Atlas-based Learning from Manual

Segmentation

Unsupervised - Abnormal tissue

Unsupervised - Lesion properties

% o

f S

tud

ies

3)Lladó et al. Information Sciences. 2013



Novelty

Lesions

Type I (across WM/GM)

WM

Cortical

• WM and cortical lesion

Segmentation

Type II (whithin the cortex)

Type III (sub-pial lesions over

one gyrus)



Novelty


Segmentation

• Applied to patients with

low disability



Novelty


Segmentation


low disability

State-of the art



Novelty


Segmentation


low disability

State-of the art

Our Study



Novelty

MPRAGE FLAIR


Segmentation


low disability

• Explore advanced MRI

contrasts

Clinical

Contrasts



Novelty

MPRAGE FLAIR

MP2RAGE DIR


Segmentation


low disability

• Explore advanced MRI

contrasts

Clinical

Contrasts

Advanced

Contrasts4)

4)Kober et al. Investigative Radiology. 2012



Outline

Multiple Sclerosis

Introduction


Conclusions

Future Trends



MRI protocol

Voxel Size, mm3

FoV, mm3

Acquisition, min.

TR, ms

TE, ms

TI, ms

MPRAGE

1.0 x 1.0 x 1.2

256 x 240 x 160

5:12

2300

2.98

900

MP2RAGE

1.0 x 1.0 x 1.2

256 x 240 x 176

8:22

5000

2.89

700/2500

3D FLAIR

1.0 x 1.0 x 1.2

256 x 240 x 176

6:27

5000

394

1800

3D DIR

1.1 x 1.0 x 1.2

256 x 240 x 160

12:52

10000

218

450/3650

Total Acquisition time, min: 32:53

3T MAGNETOM Trio a

Tim system (Siemens

Healthcare, Germany)



Pipeline5)

Pre-processing

Classification

Post-Processing

Rigid Registration

Skull-striping

Bias field correction

Intensity Normalization

Atlases registration k-Nearest Neighbors

FP reduction

Region Growing

5)Fartaria et al. JMRI. 2015



Pipeline5)

Pre-processing

Classification

Post-Processing

Rigid Registration6)

Skull-striping




FP reduction

Region Growing

DIR FLAIR MPRAGE

MP2RAGE space

Rigid Registration to

5)Fartaria et al. JMRI. 2015 6)using Elastix. S. Klein et al. IEEE Trans Med Imaging. 2010



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)




FP reduction

Region Growing


7)using Morphobox. Schmitter et al. Neuroimage:Clinical. 2015.



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)

Bias field correction8)



FP reduction

Region Growing

5)Fartaria et al. JMRI. 2015 8)using module N4ITK from 3DSlicer. Tustison et al. IEEE Trans Med Imaging. 2010.

Bias field



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)


Intensity Normalization9)


FP reduction

Region Growing


9)Nyul et al. IEEE Trans Med Imaging. 2000.

Voxel Intensity

Subject A

Subject B

Subject C

Voxel Intensity



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)



Atlases10) registration

k-Nearest Neighbors

FP reduction

Region Growing


10)ICBM 452 T1 Atlas.

MP2RAGE space

Template space Non-rigid

Registration

WM GM CSF



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)



Atlases10) registration k-Nearest Neighbors

FP reduction

Region Growing

Feature Extraction:

- Voxel intensities

- Spatial location11)

- Tissue PRIORs12)

k-NN, Voxel-wise Classification

Training Set:

• Features

• Labels (Manual Segmentations)

Lesion Probability Mask


11)Anbeek et al. Neuroimage. 2004; 12)Steenwijk et al. Neuroimage:Clinical. 2013;



Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)




False-positive reduction

Region Growing

Single voxels classified

as lesion are removed




Pipeline5)

Pre-processing

Classification

Post-Processing


Skull-stripping7)




False-positive reduction

Region Growing

Processing Time: ≈ 25 min, (not optimized)

Stop criteria

based on

MP2RAGE

intensities




Data

39 MS patients

25♀14♂

median 34, range 20 - 60

Relapsing-Remitting MS

<5 years from diagnosis (early-stages)

median 1.5, range 1 - 2

Sex

Age

Type of MS

Disease duration

EDSS score



Validation

Contribution of Advanced MRI Contrasts:

Clinical protocol:

• 3D FLAIR

• 3D FLAIR + MPRAGE

Advanced Protocol:

• 3D FLAIR + MP2RAGE

• 3D FLAIR + MPRAGE + DIR

• 3D FLAIR + MP2RAGE + DIR

Method:

“Leave-one-out” cross validation on 39 subjects

Ground Truth:

• Manual Segmentations

Metrics:

• Detection rate – number of detected lesions per brain

• Dice Similarity Coefficient – overlap measure



Segmentation Results

Type of Lesion Manual

Segmentation

Automatic

Detection

Automatic

Segmentation

White Matter

Type I

(across WM/GM)

Type II

(within the cortex)

Type III

(sub-pial lesions

over one gyrus)

Co

rtic

al L

esio

ns

MP2RAGE



Segmentation Results

Manual Segmentation Automatic Segmentation

MP2RAGE 3D FLAIR



Detection Rate WM lesions

Acquisition Time,

min.

6:27 11:39 14:49 24:31 27:41

FLAIR FLAIR

MPRAGE

FLAIR

MP2RAGE

FLAIR

MPRAGE

DIR

FLAIR

MP2RAGE

DIR

Dete

cti

on

Rate

Good Detection Rate: FLAIR + MPRAGE (Clinical Protocol)5) 5)Fartaria et al. JMRI. 2015



Detection Rate – Cortical lesions

FLAIR FLAIR

MPRAGE

FLAIR

MP2RAGE

FLAIR

MPRAGE

DIR

FLAIR

MP2RAGE

DIR

Dete

cti

on

Rate

Acquisition Time,

min.

6:27 11:39 14:49 24:31 27:41

Best Detection Rate: FLAIR + MPRAGE/MP2RAGE + DIR (Advanced Protocol)5)




Results – All type of lesions

FLAIR FLAIR

MPRAGE

FLAIR

MP2RAGE

FLAIR

MPRAGE

DIR

FLAIR

MP2RAGE

DIR

Dic

e S

imil

ari

ty

Co

eff

icie

nt

Acquisition Time,

min.

6:27 11:39 14:49 24:31 27:41

Overlap improvement : FLAIR + MPRAGE/MP2RAGE + DIR (Advanced Protocol)5)




Comparison with other Published Methods

LesionTOADS14) LST – SPM13)

13)Shiee et al. NeuroImage. 2015 14)Schmidt et al. NeuroImage. 2015

Only applied for WM lesion segmentation




Manual Segmentation Our Method LST – SPM13) LesionTOADS14)




Outline

Multiple Sclerosis

Introduction


Conclusions

Future Trends



Conclusions

• Method for WM and cortical lesion detection/segmentation based on advanced MRI

• FLAIR + advanced sequences: best detection rate for WM and cortical lesions

• Advanced sequences : long acquisition time (DIR, ca. 12:52 min)

• Clinical protocol (FLAIR + MPRAGE): Good detection rate for WM lesions

• Advanced protocol improves the overlap metric:

True Positives

False Positives

• Challenges in cortical lesion detection:

Low number of lesions

Small size

Affected by partial volume (location at WM/GM interface)



Outline

Multiple Sclerosis

Introduction


Conclusions

Future Trends



Longitudinal Assessment

Time Point 2 (TP2) Time Point 2 (TP2)

2 YEARS



Longitudinal Assessment

TP1

New Enlarged Resolved Shrunken Unaltered

Lesion Mask TP1 TP2 Lesion Mask TP1 TP2



Ultra-high field MRI (7T)

TP1 TP2

WM lesion Cortical Lesion Type I

Cortical Lesion Type II Cerebellum lesion in the folia



The Team

Advanced Clinical Imaging Technology (ACIT)

• Tobias Kober

• Gunnar Krueger

• Alexis Roche

• Bénédicte Maréchal

• Davide Picinni

• Guillaume Bonnier

• Tom Hilbert

• Pavel Falkovskiy

• Maryna Babayeva

•Théo Galy-Fajou

• Emilie Mussard

• Jannik Schildknecht

Massachusetts General Hospital and Harvard

Medical School

• Cristina Granziera

Medical Image Analyses Laboratory (MIAL)

• Meritxell Bach Cuadra

• Elena Najdenovska

• Carlos Ciller

• Sébastien Tourbier

automated detection of wm and cortical lesions in early ... · 2015-11-24, fartaria m.j. page 29...

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