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A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences
Blandine Romain1, 2, 5, Véronique Letort1, Olivier Lucidarme3, Laurence Rouet2, and Florence d’Alché-Buc4, 5
1MAS, Ecole Centrale Paris, Chatenay-Malabry, France
2Philips Research, Suresnes, France
3Hospital La Pitie-Salpetriere, AP-HP, Paris, France
4INRIA-Saclay, LRI CNRS 8623, Orsay, France
5IBISC, University of Evry, Evry, France
Abstract. Today’s follow-up of patients presenting abdominal tumors is generally performed through acquisition of dynamic sequences of contrast-enhanced CT. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumor physiology, but is impeded by the high level of noise inherent to the acquisition conditions. To improve the quality of estimation, we consider parameter estimation in voxels as a multi-task learning problem (one task per voxel) that takes advantage from the similarity between two tasks. We introduce a temporal similarity between tasks based on a robust distance between observed contrast-intake profiles of intensity. Using synthetic images, we compare multi-task learning using this temporal similarity, a spatial similarity and a single-task learning. The similarities based on temporal profiles are shown to bring significant improvements compared to the spatial one. Results on real CT sequences also confirm the relevance of the approach.
Keywords: Multi-task learning, CT perfusion, model parameter estimation
LNCS 8150, p. 271 ff.
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© Springer-Verlag Berlin Heidelberg 2013