Cardoso, M

Ciccarelli, O

Cortese, R

Kearney, H

Ourselin, S

Prados Carrasco, F

Sudre, CH

Wheeler-Kingshott, C

Yiannakas, M

UCL Discovery

08/11/2017, 19*48Page 1 of 3https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=5689A fully unsupervised method for spinal cord lesion segmentation in Multiple SclerosisCarole Hélène Sudre , Ferran Prados , Rosanna Cortese , Marios Yiannakas , Hugh Kearney , Olga Ciccarelli ,Sébastien Ourselin , Claudia Angela Gandini Wheeler-Kingshott , and M. Jorge CardosoTranslational Imaging Group, CMIC, Department of Medical Physics and Biomedical Engineering, University College London, London, UnitedKingdom, UCL Institute of Neurology, Dementia Research Centre, University College London, London, United Kingdom, Queen Square MSCentre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, Department of Brain andBehavioural Sciences, University of Pavia, Pavia, Italy, Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, ItalySynopsisThe presence of focal lesions in the spinal cord is an important diagnostic criteria for Multiple Sclerosis (MS). Accurate estimation of lesionvolume is important for monitoring disease progression over time. However, manual and automated lesion segmentation for volumeestimation remain challenging, since these rely either on the skills of the rater or the automated criteria set within the algorithms,respectively. In this work, we present an adaptation, to the spinal cord, of a fully unsupervised hierarchical model selection framework thatautomatically detects abnormality tissue patterns in the spinal cord without any pathological a priori knowledge.IntroductionSpinal cord (SC) lesions are present in most Multiple Sclerosis (MS) patients; therefore, SC MRI has been included in the diagnostic criteria of MS [1].Recently, several algorithms have been developed to segment MS lesions in the brain [2]. However, similar approaches are not yet available for SC.Analysis of SC MRI is challenging due to the small SC dimension, the large slice thicknesses due to a high in plane resolution, the grey matter shape,subject motion and artifacts [3,4]. In this work, we present an extension of a fully unsupervised hierarchical model selection framework [5] applied tothe spinal cord, to automatically detect abnormality tissue patterns in the SC without any pathological a priori knowledge.MethodsWe analysed 22 patients with MS (13 females) with visible lesions in the SC, [2 clinically-isolated syndrome (CIS), 5 relapsing-remitting (RRMS), 8secondary-progressive (SPMS) and 7 primary progressive (PPMS)] – with mean disease duration 16.5±13.1 years and median EDSS of 6.5 (range 1-7.5). All patients underwent a 3T MR scan (Philips Achieva) which included 3D-FFE and PSIR sequences [6], both acquired at a 0.5x0.5mm  in planeresolution and 5mm slice thickness. All sequences had the center of the volume positioned at the C2-3 intervertebral disc. Lesions were manuallysegmented over PSIR images by a rater using JIM v6 (Xinapse Systems, UK) and binarised to provide a ground truth (GT). The proposed lesiondetection preprocessing pipeline is described hereafter. First, we corrected for motion between PSIR and 3DFFE; second, we used [7] to compute awhite and grey matter (WM/GM) spinal cord segmentation. Third, WM, GM and cerebrospinal fluid (CSF) priors from the PAM50 atlas [8] werepropagated into the subject native space using Spinal Cord Toolbox [9]. Fourth, all volumes were isotropically resampled using the NiftyReg softwarepackage. Then, using the subject specific mask and priors, a Gaussian mixture model with an adaptive number of components separating inliers andoutliers, was obtained following a similar model as the one described in [5], with GM and WM combined in one single class to account for the limitedcontrast between tissues. In this probabilistic framework, we sample models according to [5] in order to find a mixture model that balances model fitand complexity. Finally, after the model selection process has converged, a post-processing step was applied to select candidate lesion voxelsaccording to the distribution of the healthy appearing tissue, followed by a slice-by-slice morphological correction step used to remove spurioussegmentations. Figure 1 illustrates the steps of the method. To assess the performance of the proposed method, we computed median andinterquartile range (IQR) for diﬀerent metrics including mean surface distance in mm (MSD), Dice score coeﬃcient (DSC), sensitivity, specificity,accuracy and volume diﬀerence (VD) expressed in percentages between the obtained segmentation and the GT in the original acquisition space.ResultsFigure 2 shows a qualitative assessment of the results. A quantitative assessment showed a 80% (p<0.05) correlation between the automated lesionvolumes and the GT (see Figure 3) with a median [IQR] VD of 46.2 [17.9;71.2]. Our unsupervised method obtained a median [IQR] DSC=41.6[30.9;60.2] and MSD of 1.70 [0.87 2.43], when we compared to GT, see Figure 4. Finally, we also observed a median [IQR] sensitivity of 0.50[0.25;0.71], specificity of 99.99 [99.99;100.0] and accuracy of 99.99 [99.98;100.0], all in percentages.DiscussionThe qualitative assessment of the results highlighted the good performance of the proposed method (Figure 2). Nonetheless, we observed a fewspurious voxels in the spinal cord rim, and quantitative results suggested that disagreement between ground truth and automated methods occurredat the lesion borders. The volumes were found to correlate with the GT (p<0.05). Phenotypes with higher volume loads, especially SPMS (see Figure3), resulted in better overall performance. Given the volume of lesions compared to the overall image volume, sensitivity ratios were found to be high.ConclusionsIn this work, we have demonstrated the feasibility of automatically segmenting MS spinal cord lesions using a fully unsupervised approach based ona Bayesian model selection. The proposed method tends to extend lesions in the cord rim when compared to the ground truth, with volumes beinghighly correlated. The main advantage of this technique is that it works without any pathological a priori knowledge or training dataset, potentiallymaking its extension to other pathologies straightforward. Future work will focus on improving the biologically plausibility of the model, computing asubject-specific CSF, WM, GM and rim mask, and test the performance of the proposed method in other neurological conditions such as SC injury1,2 1,3 3 3 3 31,2 3,4,5 1,212 345208/11/2017, 19*48Page 2 of 3https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=5689and myelopathy.AcknowledgementsCS is an Alzheimer's Society Junior Fellow and FP is a Guarantors of Brain non-clinical Fellow. This project also received support from NIHR BRCUCLH/UCL High Impact Initiative, EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), MRC (MR/J01107X/1), ISRT, WfL & CHNF (INSPIRED), UKMS Society and Brain Research Trust.References1. Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., Fujihara, K., Havrdova, E., Hutchinson, M., Kappos, L., Lublin, F.D., Montalban, X., O’Connor, P., Sandberg-Wollheim, M., Thompson, A. J., Waubant, E., Weinshenker, B. & Wolinsky, J. S. Diagnostic criteria formultiple sclerosis: 2010 Revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).2. Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Prados, F., Sudre, C. H., Jorge Cardoso, M.,Cawley, N., Ciccarelli, O., Wheeler-Kingshott, C. A. M., Ourselin, S., Catanese, L., Deshpande, H., Maurel, P., Commowick, O., Barillot, C., Tomas-Fernandez, X., Warfield, S. K., Vaidya, S., Chunduru, A., Muthuganapathy, R., Krishnamurthi, G., Jesson, A., Arbel, T., Maier, O., Handels, H., Iheme,L. O., Unay, D., Jain, S., Sima, D. M., Smeets, D., Ghafoorian, M., Platel, B., Birenbaum, A., Greenspan, H., Bazin, P.-L., Calabresi, P. A., Crainiceanu,C. M., Ellingsen, L. M., Reich, D. S., Prince, J. L. & Pham, D. L. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.Neuroimage 148, (2017).3. Wheeler-Kingshott, C. A., Stroman, P. W., Schwab, J. M., Bacon, M., Bosma, R., Brooks, J., Cadotte, D. W., Carlstedt, T., Ciccarelli, O., Cohen-Adad, J., Curt, A., Evangelou, N., Fehlings, M. G., Filippi, M., Kelley, B. J., Kollias, S., Mackay, A., Porro, C. A., Smith, S., Strittmatter, S. M.,Summers, P., Thompson, A. J. & Tracey, I. The current state-of-the-art of spinal cord imaging: applications. Neuroimage 84, 1082–93 (2014).4. Stroman, P. W., Wheeler-Kingshott, C., Bacon, M., Schwab, J. M., Bosma, R., Brooks, J., Cadotte, D., Carlstedt, T., Ciccarelli, O., Cohen-Adad, J.,Curt, A., Evangelou, N., Fehlings, M. G., Filippi, M., Kelley, B. J., Kollias, S., Mackay, A., Porro, C. A., Smith, S., Strittmatter, S. M., Summers, P. &Tracey, I. The current state-of-the-art of spinal cord imaging: Methods. Neuroimage 84, 1070–1081 (2014).5. Sudre, C. H., Cardoso, M. J., Bouvy, W. H., Biessels, G. J., Barnes, J. & Ourselin, S. Bayesian Model Selection for Pathological Neuroimaging DataApplied to White Matter Lesion Segmentation. IEEE Trans. Med. Imaging 34, (2015).6. Kearney, H., Miszkiel, K. A., Yiannakas, M. C., Altmann, D. R., Ciccarelli, O. & Miller, D. H. Grey matter involvement by focal cervical spinal cordlesions is associated with progressive multiple sclerosis. Mult. Scler. J. 22, 910–920 (2016).7. Prados, F., Cardoso, M. J., Yiannakas, M. C., Hoy, L. R., Tebaldi, E., Kearney, H., Liechti, M. D., Miller, D. H., Ciccarelli, O., Wheeler-Kingshott, C. A.M. G. & Ourselin, S. Fully automated grey and white matter spinal cord segmentation. Sci. Rep. 6, 36151 (2016).8. De Leener, B., Fonov, V., Collins, L., Callot, V., Stikov, N. & Cohen-Adad, J. PAM50: Multimodal template of the brainstem and spinal cordcompatible with the ICBM152 space. in Proc. 25th Annu. Meet. ISMRM 165, 170–179 (Academic Press, 2017).9. De Leener, B., Lévy, S., Dupont, S. M., Fonov, V. S., Stikov, N., Louis Collins, D., Callot, V. & Cohen-Adad, J. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage 145, 24–43 (2017).FiguresSchema representing the core steps of the presented framework with intermediate outputsFrom left to right, PSIR input image, overlaid manual segmentation (green), segmentation results by the proposed unsupervised method (red)08/11/2017, 19*48Page 3 of 3https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=5689Graph showing the correlation between the ground truth and the obtained lesion volumes in mm3 (blue line, and in grey the 95% CI). Each dotrepresents a subject and the dot's color is the MS phenotype of each subjectMean Surface Distance in mm (smaller means better) and Dice score coeﬃcient (bigger means better) per phenotype, each dot represents theobtained result for each individual subject..

A fully unsupervised method for spinal cord lesion segmentationin multiple sclerosis

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A fully unsupervised method for spinal cord lesion segmentationin multiple sclerosis

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