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The objective of this study is to develop a patch-based labeling method thatcooperates with a label fusion using non-rigid registrations. We present anovel patch-based label fusion method, whose selected patches and their weightsare calculated from a combination of similarity measures between patches usingintensity-based distances and labeling-based distances, where a previouslabeling of the target image is inferred through a label fusion method usingnon-rigid registrations. These combined similarity measures result in betterselection of the patches, and their weights are more robust, which improves thesegmentation results compared to other label fusion methods, including theconventional patch-based labeling method. To evaluate the performance and therobustness of the proposed label fusion method, we employ two availabledatabases of T1-weighted (T1W) magnetic resonance imaging (MRI) of humanbrains. We compare our approach with other label fusion methods in theautomatic hippocampal segmentation from T1W-MRI.  Our label fusion method yields mean Dice coefficients of 0.847 and 0.798 forthe two databases used with mean times of approximately 180 and 320 seconds,respectively. The collaboration between the patch-based labeling method and thelabel fusion using non-rigid registrations is given in the several levels: (a)The pre-selection of the patches in the atlases are improved, (b) The weightsof our selected patches are also more robust, (c) our approach imposesgeometrical restrictions, such as shape priors, and (d) the work-flow is veryefficient. We show that the proposed approach is very competitive with respectto recently reported methods.

Combining patch-based strategies and non-rigid registration-based label fusion methods