Producción CyT
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration
Artículo
Fecha:
2019Editorial y Lugar de Edición:
Institute of Electrical and Electronics Engineers Inc.Revista:
IEEE Journal of Biomedical and Health Informatics, vol. 23 (pp. 1374-1384) - ISSN 2168-2194Institute of Electrical and Electronics Engineers Inc.
ISSN:
2168-2194Resumen
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain-specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines. The learned matching criterion is integrated within a metric-free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.Palabras Clave
WEAKLY SUPERVISED LEARNINGLATENT STRUCTURED SUPPORT VECTOR MACHINE (LSSVM)DISCRETE GRAPHICAL MODELSDEFORMABLE IMAGE REGISTRATION