Hippocampus segmentation by optimizing the local contribution of image and prior terms, through graph cuts and multi-atlas

This paper presents a new method for segmentation of ambiguously defined structures, such as the hippocampus, by exploiting prior knowledge from another perspective. An expert´s experience of where to use prior knowledge and where image information, is captured as a local weighting map. This map can be used to locally guide the evolution in a level set evolution framework. Such a map is produced for every training image using Graph-cuts to calculate the most suited balance of current and prior information. Training maps are optimally adapted on the test image, through non-rigid registration, producing the Optimum Local Weighting map, which is anatomically the most suitable to this test image. Experimental results demonstrate the efficacy and accuracy of the proposed method.