1. Deformable Registration through Learning of Context-Specific Metric Aggregation
- Author
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Ferrante, Enzo, Dokania, Puneet K, Marini, Rafael, Paragios, Nikos, Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Ecole Centrale Paris, Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec, Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London, University of Oxford, Ecole Centrale Paris-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and University of Oxford [Oxford]
- Subjects
FOS: Computer and information sciences ,Computer Science - Learning ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Machine Learning (cs.LG) - Abstract
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities., Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 2017
- Published
- 2017