Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling.

Hosung Kim,Neda Bernasconi,Andrea Bernasconi,Benoit Caldairou

doi:10.3389/fninf.2018.00039

Abstract

Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent multiple-template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large template library, as segmentation suffers when the boundaries of template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-template segmentation, HybridMulti could maintain accurate performance even with a 50% template library size.

Highlights

Mesiotemporal lobe (MTL) structures, such as the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC), undergo marked morphological changes in numerous neurological and neuropsychiatric conditions (Wang et al, 2010; Cavedo et al, 2011; Bernhardt et al, 2013; Shi et al, 2013; Joo et al, 2014; Maccotta et al, 2015; Arnone et al, 2016)
In temporal lobe epilepsy (TLE), the most common surgically-amenable epilepsy in adults, manual MRI volumetry allows defining the side of mesiotemporal atrophy in up to 70– 90% of patients (Schramm and Clusmann, 2008), and thereby help identifying the surgical target
Existing automatic segmentation algorithms produce excellent segmentation results for HP and AM in healthy controls (Collins and Pruessner, 2010), their performance in TLE is challenged by the combined effects of atrophy and positional abnormalities (Kim et al, 2012a)

Summary

Introduction

Mesiotemporal lobe (MTL) structures, such as the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC), undergo marked morphological changes in numerous neurological and neuropsychiatric conditions (Wang et al, 2010; Cavedo et al, 2011; Bernhardt et al, 2013; Shi et al, 2013; Joo et al, 2014; Maccotta et al, 2015; Arnone et al, 2016). MRI volumetry has been the most commonly employed technique to assess MTL pathology in vivo (Goncharova et al, 2001; Bernasconi et al, 2003). In temporal lobe epilepsy (TLE), the most common surgically-amenable epilepsy in adults, manual MRI volumetry allows defining the side of mesiotemporal atrophy in up to 70– 90% of patients (Schramm and Clusmann, 2008), and thereby help identifying the surgical target. A study (Hu et al, 2014) segmented the EC, a PHG subregion considered a core epileptogenic zone in TLE (Bernasconi et al, 2003) with suboptimal accuracy (Dice index=73%), likely due to challenges imposed by its complex and variable shape

Methods

Results

Conclusion