1 research outputs found
Comparative Evaluation Of Three Methods Of Automatic Segmentation Of Brain Structures Using 426 Cases
Segmentation of brain structures in a large dataset of magnetic resonance
images (MRI) necessitates automatic segmentation instead of manual tracing.
Automatic segmentation methods provide a much-needed alternative to manual
segmentation which is both labor intensive and time-consuming. Among brain
structures, the hippocampus presents a challenging segmentation task due to its
irregular shape, small size, and unclear edges. In this work, we use
T1-weighted MRI of 426 subjects to validate the approach and compare three
automatic segmentation methods: FreeSurfer, LocalInfo, and ABSS. Four
evaluation measures are used to assess agreement between automatic and manual
segmentation of the hippocampus. ABSS outperformed the others based on the Dice
coefficient, 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 than
the others to the field inhomogeneity of 3T MRI