Hansen, Michael Sass

Larsen, Rasmus

Sjöstrand, Karl

Online Research Database In Technology

Predicting clinical outcome from image derived morphologicalchangeRasmus Larsen∗, Karl Sjoˆstrand, Michael Sass HansenInformatics and Mathematical Modelling, Technical University of DenmarkRichard Petersens Plads, bldg. 321, DK-2800 Kgs. Lyngby, Denmark{rl,kas,msh}@imm.dtu.dk, {www.imm.dtu.dk/image}In this presentation we will outline a series of results on supervised and unsupervised learning of clinicaloutcome from medical images. We will be concerned with relating dynamic shape compensated measure-ments, shape, and spatial deformations to clinical parameters. The main challenge addressed is the factthat image measurement are of a dimensionality many times higher than the number of observations.In general, this problem is solved by input variable selection, subspace methods and/or regularization.The methods that we apply operated seamlessly between these solutions and is based on seminal workby Brad Efron, Trevor Hastie, and Robert Tibshirani, Stanford University (1; 2; 3).Myocardial infarction may by quantified by analysis of myocardial perfusion magnetic resonanceimages (MRI). The images are formed by MRI of a contrast agent bolus passage through the my-ocardium. Attenuated and delayed response identifies regions affected by the infarction. Followingimage co-registration perfusion dependent intensity variation in single voxel elements are recorded overtime (4). In order to identify anomalous behavior from healthy tissue we apply the support vector domaindescriptor (SVDD) (5). The SVDD classifier is a hyper-sphere in a transformed input space. An efficientalgorithm for computing the solution for all values of a regularization parameter was proposed in (6).The infarction region is found as outliers in an SVDD procedure integrating information across all valuesof the regularization parameter (7).In two studies brain MRI is related to clinical parameters such as memory scores, walking speed, andverbal fluency. Following mid-sagittal surface extraction (8) and segmentation of the corpus callosum inthe mid-sagittal plane (9) the clinical parameters are regressed on localized shape components of varianceidentified using sparse principal components analysis (10). In a second study full brain deformation fieldsacross populations are related to memory loss using a ridge regression procedure with permutation testingtaking into account voxel dependencies.[1] R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society - Series BMethodological, vol. 58, no. 1, pp. 267–288, 1996.[2] B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Annals of Statistics, vol. 32, no. 2,pp. 407–451, 2004.[3] H. Zou, T. Hastie, and R. Tibshirani, “Sparse principal component analysis,” Journal of Computational and GraphicalStatistics, vol. 15, no. 2, p. 265, 2006.[4] M. B. Stegmann, H. O´lafsdo´ttir, and H. B. W. Larsson, “Unsupervised motion-compensation of multi-slice cardiacperfusion MRI,” Medical Image Analysis, vol. 9, pp. 394–410, aug 2005.[5] D. M. Tax and R. P. Duin, “Support vector domain description,” Pattern Recognition Letters, vol. 20, no. 11-13,pp. 1191–1199, 1999.[6] K. Sjo¨strand and R. Larsen, “The entire regularization path for the support vector domain description,” Proc. of. 9thInt. Conf. on Medical Image Analysis and Computer Assisted Intervention, LNCS, vol. 4190, pp. 241–248, 2006.[7] M. S. Hansen, H. O´lafsdo´ttir, K. Sjo¨strand, H. B. Larsson, M. B. Stegmann, and R. Larsen, “Ischemic segmentdetection using the support vector domain description,” Proceedings of the SPIE - The International Society forOptical Engineering, vol. 6512, no. 14, 2007.[8] M. Stegmann, K. Skoglund, and C. Ryberg, “Mid-sagittal plane and mid-sagittal surface optimization in brain mri usinga local symmetry measure,” Proceedings of the SPIE - The International Society for Optical Engineering, vol. 5747,no. 1, pp. 568–79, 2005.[9] M. Stegmann, R. Davies, and C. Ryberg, “Corpus callosum analysis using mdl-based sequential models of shape andappearance,” Proceedings of the SPIE - The International Society for Optical Engineering, vol. 5370, no. 1, pp. 612–19,2004.[10] K. Sjo¨strand, E. Rostrup, C. Ryberg, R. Larsen, C. Studholme, H. Baezner, J. Ferro, F. Fazekas, L. Pantoni, D. Inzitari,and G. Waldemar, “Sparse decomposition and modeling of anatomical shape variation,” IEEE Transactions on MedicalImaging (to appear), 2007.∗Corresponding author, Ph.: +45 45253415, Fax +45 458813971

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KEYNOTE ADDRESS: Predicting clinical outcome from image derived morphological

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