The development of tools for a fully automatic segmentation of the relevant brain structures, such as the hippocampus, is potentially very useful for pathologies detection. In this paper, a method for the automated hippocampal segmentation, based on virtual ant colonies, is proposed. The algorithm used, the Channeler Ant Model (CAM), represents an effective way to segment 3D objects with a complex 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 segmentation 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.

Fully automated hippocampus segmentation with virtual ant colonies

DE MITRI, IVAN
2012-01-01

Abstract

The development of tools for a fully automatic segmentation of the relevant brain structures, such as the hippocampus, is potentially very useful for pathologies detection. In this paper, a method for the automated hippocampal segmentation, based on virtual ant colonies, is proposed. The algorithm used, the Channeler Ant Model (CAM), represents an effective way to segment 3D objects with a complex 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 segmentation 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.
2012
9781467320498
Medical Imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12571/3335
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