Statistical validation of automatic methods for hippocampus segmentation in MR images of epileptic patients

Abstract

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

The bumps under the hippocampus

“…Hosseini et al provided a comprehensive evaluation of the hippocampus segmentation algorithms [Hosseini et al, ]. They categorize various metrics into three groups.…”

“…The three groups of metrics base their evaluation on, respectively, voxel, distance, and volume. To better characterize the segmentation with and without the super resolution scheme, we also compute the evaluation metrics in [Hosseini et al, ], shown in Table .…”

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

“…Few reports of automated hippocampal segmentation in cases affected by mTLE have been published. 20,39,47,48 In this study, four automated methods were compared against the manual approach using 195 mTLE cases to provide a robust comparative analysis of their respective performances. Analysis of voxel-, distance-, and volume-based metrics shows that ABSS and LocalInfo are more accurate segmentation methods in the case of mTLE.…”

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

“…Given the current evolution in the field of automated segmentation, a modular approach to this methodology allows alternate automated segmentation methods including HAMMER, Localinfo, ANIMAL-multi, SACHA, FS+LDDMM or ABSS, 38,48,49 to be used in a similar fashion.…”