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An An aut automated ed pr probabilis babilistic ic alg algorit ithm hm for the he de detectio ection n of cen centr tral al vein ein sig ign n in in whit hite-ma matter lesion

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March 1, 2019 Jordan Dworkin

Sati et al., 20164

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Motivation

• Can automated methods help detect whether or not a lesion has a central vein?
• Why is this important?
• A high proportion of central vein lesions seems to be highly specific to MS, compared to other diseases

with white matter lesions4

• Rigorous definitions of what qualifies as a central vein make manual determination a time intensive

process, which has lead some to recommend picking a random sample of 3 lesions for judgement

• Less rigid definitions would lessen the burden, but exacerbate subjective differences in judgements
• Goal
• To create a method that automatically detects central vein sign in white matter lesions

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Proposed method

• Why build a rigid, untrained algorithm?
• More control over what features it considers important
• Potentially more robust to application at different sites
• Yields probabilistic estimate of CVS
• Points clinicians towards lesions where the model is less confident
• Algorithm steps:
• Find white-matter lesions
• Find veins
• Determine centrality of veins within lesions

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1) Find white-matter lesions 2) Find veins 3) Determine centrality

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1) Find white-matter lesions 2) Find veins 3) Determine centrality

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Campbell et al., 20152

1) Find white-matter lesions 2) Find veins 3) Determine centrality

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1) Find white-matter lesions 2) Find veins 3) Determine centrality

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1) Find white-matter lesions 2) Find veins 3) Determine centrality

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1) Find white-matter lesions 2) Find veins 3) Determine centrality

Permutation procedure

• Examining coherence between

vesselness, !

"#$, and centrality,

%"#$, of voxels in a lesion

• Subject i, lesion j’s coherence:
• &"# = ∑$∈* %"#$ ∗ !

"#$

• Want to find the probability of

seeing this level of coherence by chance

• For , ∈ {1, … , 12}
• &"#2

∗

= ∑$∈*,4∈*

5 ∗ %"#$ ∗ !

"#4

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Permutation procedure

• Examining coherence between

vesselness, !

"#$, and centrality,

%"#$, of voxels in a lesion

• Subject i, lesion j’s coherence:
• &"# = ∑$∈* %"#$ ∗ !

"#$

• Want to find the probability of

seeing this level of coherence by chance

• For , ∈ {1, … , 12}
• &"#2

∗

= ∑$∈*,4∈*

5 ∗ %"#$ ∗ !

"#4

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Pcvs = 0.99

Performance assessment

• Tested algorithm on sample of 39 participants at the University of Vermont5
• 10 had MS with no white-matter comorbidities
• 10 had MS with white-matter comorbidities
• 10 had migraine with white-matter abnormalities
• 9 were previously incorrectly diagnosed with MS
• Wanted to determine whether algorithm would find a higher proportion of central vein lesions

in MS patients, and whether it could be diagnostically effective

• Sought to test two versions of the algorithm:
• 1 – Semi-automated; lesion segmentation is checked and artifacts are removed before CVS detection
• 2 – Fully-automated; no QA following segmentation

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Fully–automated marker

0.00 0.25 0.50 0.75 1.00 Migraine Misdiagnosed Multiple sclerosis (with comorbidities) Multiple sclerosis (no comorbidities)

Group Fully−automated biomarker MS

MS

No Yes

Performance assessment

• Fully-automated: Within-patient proportions
• f CVS were significantly higher in MS patients

than non-MS patients

• Mean CVS proportion in MS patients: 53%
• Mean CVS proportion in non-MS patients: 34%

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Performance assessment

• Fully-automated: Within-patient proportions
• f CVS were significantly higher in MS patients

than non-MS patients

• Mean CVS proportion in MS patients: 53%
• Mean CVS proportion in non-MS patients: 34%
• Semi-automated: Differences were more

pronounced after removing false-positives from lesion segmentation

• Mean CVS proportion in MS patients: 57%
• Mean CVS proportion in non-MS patients: 27%

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0.00 0.25 0.50 0.75 1.00 Migraine Misdiagnosed Multiple sclerosis (with comorbidities) Multiple sclerosis (no comorbidities)

Group Semi−automated marker MS

MS

No Yes

0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00

Specificity Sensitivity Method

AUC = 0.87 AUC = 0.81

Method

Fully−auto Semi−auto

Performance assessment

• Semi-automated marker had high sensitivity

and specificity in distinguishing MS from non- MS participants

• Optimal sensitivity/specificity = 0.75/0.95
• AUC = 0.87
• Fully-automated marker also distinguished

MS from non-MS participants, but tended to have stronger sensitivity than specificity

• Optimal sensitivity/specificity = 0.85/0.63
• AUC = 0.81

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Summary

• This study developed an automated method for detecting CVS in white matter lesions
• Previous findings that CVS occurs at a higher rate in MS subjects are replicated
• Performance provides preliminary evidence for the algorithm’s validity and potential diagnostic utility
• Proposed method builds on existing lesion and vein segmentation tools
• Allows for the use of site-specific methods, and the continuous implementation of the newest

segmentation methods

• Could reduce the prevalence of false-positive lesions, and bring the fully-automated results closer to the semi-automated results

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References

1. Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp S, eds. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. Vol 1496. Berlin, Heidelberg: Springer Berlin Heidelberg; 1998:130 -137 2. Campbell IC, Coudrillier B, Mensah J, Abel RL, Ethier CR. Automated segmentation of the lamina cribrosa using Frangi’s filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye. J R Soc Interface. 2015;12(104):20141009-20141009 3. Sweeney EM, Shinohara RT, Shea CD, Reich DS, Crainiceanu CM. Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI. Am J Neuroradiol. 2013;34(1):68-73 4. Sati P, Oh J, Constable RT, et al. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol. 2016;12(12):714-722 5. Solomon AJ, Watts R, Ontaneda D, Absinta M, Sati P, Reich DS. Diagnostic performance of central vein sign for multiple sclerosis with a simplified three- lesion algorithm. Mult Scler Houndmills Basingstoke Engl. August 2017 6.

• E. M. Sweeney, R. T. Shinohara, N. Shiee, F. J. Mateen, A. A. Chudgar, J. L. Cuzzocreo, P. A. Calabresi, D. L. Pham, D. S. Reich, and C. M. Crainiceanu, “OASIS is

Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI,” NeuroImage Clin., vol. 2, pp. 402– 413, 2013 7.

• E. M. Sweeney, R. T. Shinohara, B. E. Dewey, M. K. Schindler, J. Muschelli, D. S. Reich, C. M. Crainiceanu, and A. Eloyan, “Relating multi-sequence

longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions,” NeuroImage Clin., vol. 10, pp. 1–17, 2016

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Acknowledgements

• University of Pennsylvania
• Taki Shinohara, Ali Valcarcel, and the PennSIVE group
• Ipek Oguz and PICSL
• Amit Bar-Or, Matthew Schindler, and the Penn MS Division
• NINDS
• Daniel Reich, Pascal Sati, and Dzung Pham
• University of Vermont
• Andrew Solomon and Richard Watts
• NAIMS Cooperative

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