Abstract

ObjectiveThe objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).Materials and methodsFAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).ResultsWith only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.ConclusionsFASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.

Highlights

  • Hippocampus segmentation on structural magnetic resonance images (MRI) is used to monitor morphological hippocampal changes which occur in diseases like Alzheimer’s disease (AD), depression, epilepsy, and schizophrenia [1,2,3,4]

  • We compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST)

  • Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF

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Summary

Introduction

Hippocampus segmentation on structural magnetic resonance images (MRI) is used to monitor morphological hippocampal changes which occur in diseases like Alzheimer’s disease (AD), depression, epilepsy, and schizophrenia [1,2,3,4]. The hippocampus is a small archicortical brain structure which shows limited contrast on structural MRI scans because adjacent structures, such as the amygdala, caudate nucleus and the thalamus typically have similar intensity [12]. This makes hippocampus segmentation a difficult task, regardless of the degree of automation used. According to Dill et al, the reasons why these methods are still not ready for routine clinical use include the sensitivity of automatic methods to the choice of (patient group dependent) atlases, the computational cost of multiple atlas registration, the lack of validation for different data sets, and the complexity of the required manual pre- and post-processing procedures [13]

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