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Segmentation of brain structures in a large dataset of magnetic resonanceimages (MRI) necessitates automatic segmentation instead of manual tracing.Automatic segmentation methods provide a much-needed alternative to manualsegmentation which is both labor intensive and time-consuming. Among brainstructures, the hippocampus presents a challenging segmentation task due to itsirregular shape, small size, and unclear edges. In this work, we useT1-weighted MRI of 426 subjects to validate the approach and compare threeautomatic segmentation methods: FreeSurfer, LocalInfo, and ABSS. Fourevaluation measures are used to assess agreement between automatic and manualsegmentation of the hippocampus. ABSS outperformed the others based on the Dicecoefficient, precision, Hausdorff distance, ASSD, RMS, similarity, sensitivity,and volume agreement. Moreover, comparison of the segmentation results,acquired using 1.5T and 3T MRI systems, showed that ABSS is more sensitive thanthe others to the field inhomogeneity of 3T MRI.

Comparative Evaluation Of Three Methods Of Automatic Segmentation Of Brain Structures Using 426 Cases