Fully automated hippocampus segmentation with virtual ant colonies

The development of tools for a fully automatic segmenta- tion of the relevant brain structures, such as the hippocam- pus, is potentially very useful for pathologies detection. In this paper, a method for the automated hippocampal seg- mentation, based on virtual ant colonies, is proposed. The algorithm used, the Channeler Ant Model (CAM), rep- resents an effective way to segment 3D objects with a com- plex shape in a noisy background. The CAM was modified by inserting a shape knowledge that is crucial to face the hippocampus segmentation. The algorithm was trained and tested using a database of 56 T1 weighted MRI images with a known manual segmen- tation of the hippocampus volume. The results are comparable to other methods: an average Dice Index of 0.74 and 0.72 is obtained over the left and right hippocampi, respectively. The lack of a heavy training procedure, because all the model parameters are fixed, and the speed make this approach very effective.