title: A multi-task learning approach for compartmental model parameter estimation in DCE-CT sequences.
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.
authors.affiliation: MAS, Ecole Centrale Paris, Chatenay-Malabry.
authors.forename: Blandine
authors.initials: B
authors.lastname: Romain
authors.affiliation: MAS, Ecole Centrale Paris, Chatenay-Malabry.
authors.forename: Véronique
authors.initials: V
authors.lastname: Letort
authors.affiliation: Hospital La Pitie-Salpetriere, AP-HP, Paris.
authors.forename: Olivier
authors.initials: O
authors.lastname: Lucidarme
authors.affiliation: Philips Research, Suresnes.
authors.forename: Laurence
authors.initials: L
authors.lastname: Rouet
authors.affiliation: INRIA-Saclay, LRI CNRS 8623, Orsay, France.
authors.forename: Florence
authors.initials: F
authors.lastname: Dalché-Buc
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009369:Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*