Pseudo-Healthy Image Synthesis for WhiteMatter Lesion Segmentation

Christopher Bowles1, Chen Qin1, Christian Ledig1, Ricardo Guerrero1,

Roger Gunn2,7, Alexander Hammers3, Eleni Sakka4, David Alexander

Dickie4, Maria Valdes Hernandez4, Natalie Royle4,8, Joanna Wardlaw4,

Hanneke Rhodius-Meester5, Betty Tijms5, Afina W. Lemstra5, Wiesje

van der Flier5, Frederik Barkhof6, Philip Scheltens5 and Daniel Rueckert1

1Department of Computing, Imperial College London, UK2Imanova Ltd., London, UK3PET Centre, King’s College London, UK4Department of Neuroimaging Sciences, University of Edinburgh, UK5Alzheimer center and department of Neurology, VU University Medical Center, Amsterdam Neuroscience,Amsterdam, the Netherlands6Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam Neuroscience,Amsterdam, the Netherlands7Department of Medicine, Imperial College London, UK8IXICO Technologies Ltd., London, UK

Pseudo-Healthy Image Synthesis for WML Segmentation 1

T1 FLAIR

Pseudo-Healthy Image Synthesis for WML Segmentation 2

T1 FLAIR

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What are our options?

Roy et al. ’10,’11,’13,’14; Cao et al ’13, ’14; Huang et al ’13; Ye et al ’13;Commowick ’09

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What are our options?

Jog et al. ’13; Glocker et al. ’16

Pseudo-Healthy Image Synthesis for WML Segmentation 5

What are our options?

Van Nguyen et al. ’15; Vemulapalli et al. ’15

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What are our options?

Kroon et al. ’09

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What are our options?

Pseudo-Healthy Image Synthesis for WML Segmentation 8

Plenty of options – What’s the problem?

T1 FLAIR

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Plenty of options – What’s the problem?

I Most methods use intensity information from the whole brainor a large window

I Lesions and grey matter intensities can have a similarrelationship in T1 and FLAIR (dark on T1, bright on FLAIR)

I Atlas registration will be strongly influenced by pathologyI Deep learning has potential

I Little work in this areaI Has practical implications (hardware)

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Proposed Solution?

I Training: 1-10h

I Synthesis: <1s

I Voxel-wise kernel regression

R(a, b, k) =

∑i (K((k − ai )/h)bi )∑

i K((k − ai )/h),

(1)

K(p) =1√2π

e−12p2 . (2)

I Using a co-registerednon-pathological training set

I Learn a separate model for eachvoxel

I Use information from a smallpatch around the voxel (5x5x5)

I Each model will only beinfluenced by this small region

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Segmentation - Establish an error range

FLAIR Synthetic Difference

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Segmentation - Comparison with synthesised image

FLAIR Synthetic LikelihoodSynth

Error map

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Segmentation - False positive prevention

LikelihoodFlair

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Segmentation - Combining likelihood maps

LikelihoodSynth LikelihoodFlair

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Segmentation - Likelihood map

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Segmentation - Refinement

I Many ways of binarizing likelihood mapI ThresholdI CRFI Region growing

I Proposed method uses two thresholds:I First to locate large, lower intensity regionsI Second to locate small, high intensity regionsI Followed by region growing to lesion boundaries

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Results

I Comparison with Lesion Segmentation Toolbox 1

I Lesion Growth Algorithm (LGA)I Lesion Prediction Algorithm (LPA)

I Methods tested with 42 subjects with reference segmentations

Method ASSD DSC HD LC

LGA 5.89 0.367 40.3 0.760LPA 2.58 0.599 33.2 0.711Proposed 2.39 0.603 30.1 0.849

ASSD: Average Symmetric Surface DistanceDSC: Dice Similarity CoefficientHD: Hausdorff distanceLC: Correlation between volumes of calculated and reference loads

1http://www.applied-statistics.de/lst.html

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Visual Comparisons with LPA

LPA Proposed Reference

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Conclusions

We have:I Presented a novel method for fast image synthesis

I Particularly suited to healthy image synthesis in the presenceof WMH

I Demonstrated synthetic images can be used to segment WMH

I Compared segmentations with some commonly used methodswith favourable results

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Conclusions

We have:I Presented a novel method for fast image synthesis

I Particularly suited to healthy image synthesis in the presenceof WMH

I Demonstrated synthetic images can be used to segment WMH

I Compared segmentations with some commonly used methodswith favourable results

Thank you - Any Questions?

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