M. WimmerD. Major ,  A. A. Novikov ,  K. Bühler (2016)

Local entropy-optimized texture models for semi-automatic spine labeling in various MRI protocols

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2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)


We present a novel pipeline for acquisition protocol independent spine labeling in volumetric Magnetic Resonance Imaging (MRI) data of the lumbar spine. Our learning-based system uses local Entropy-optimized Texture Models (ETMs) for reducing the intensity scale in clinical data to only a few gray levels. The task of intervertebral disc localization is then performed on the normalized data. The benefit of our method is, that we can deal with various MRI protocols, such as T1-weighted (T1w) and T2-weighted (T2w) scans. Using the entropy objective allows us furthermore to apply the algorithm to acquisition protocols which are not covered by the training set. We achieve high disc localization accuracies for both, MRI protocols which are covered and not covered by training. The approach can be easily extended to other modalities.

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research groups



Adaptation models;Data models;Labeling;Magnetic resonance imaging;Protocols; Solid modeling;Training;Entropy-optimized Texture Models;MRI;Spine labeling