Accurate and fully Automatic Hippocampus Segmentation using subject-specific 3D Optimal Local Maps into a hybrid Active Contour Model
Assessing the structural integrity of the hippocampus (HC) is an essential step towards prevention, diagnosis and follow-up of various brain disorders due to the implication of the structural changes of HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably and reproducibly segment the HC, has attracted considerable attention over the past decades. This paper presents an innovative 3D fully automatic method to be used on top of the multi-atlas concept for the HC segmentation. The method is based on a subject-specific set of 3D Optimal Local Maps (OLMs) that locally control the influence of each energy term of a hybrid Active Contour Model (ACM). The complete set of OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available datasets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method.