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

TL;DR

This study compares three automatic brain segmentation methods using 426 MRI cases, finding ABSS to be the most accurate and sensitive, especially across different MRI field strengths.

Contribution

It provides a comprehensive validation and comparison of three automatic hippocampal segmentation methods on a large dataset, highlighting ABSS's superior performance.

Findings

ABSS outperforms FreeSurfer and LocalInfo in segmentation accuracy.

ABSS is more sensitive to MRI field inhomogeneity.

The study validates segmentation methods on a large, diverse dataset.

Abstract

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.