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

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.

Automated segmentation of medial temporal lobe subregions on in vivo T1‐weighted MRI in early stages of Alzheimer's disease

“…In contrast to most previous methods for T1w MRI, our multiatlas approach for labeling MTL cortical regions takes into account the anatomical variability of the MTL cortex, which is known to influence the locations of borders between MTL cortical regions. The accuracy of the reported DSC values in the same range (Hu, Coupé, Pruessner, & Collins, 2014;Kim, Caldairou, Bernasconi, & Bernasconi, 2018). With regard to hippocampus, our pipeline performs comparable to state-ofthe-art methods (Collins & Pruessner, 2010;Coupé et al, 2011;Leung et al, 2010;Platero & Tobar, 2016;.…”

“…ASHS-T1 demonstrated similar accuracy for the MTL cortices as the T2w-based ASHS, with a slightly lower accuracy for the ERC. Two other studies performed an evaluation of MTL cortices in younger adults and patients with temporal lobe epilepsy and reported DSC values in the same range (Hu, Coupé, Pruessner, & Collins, 2014; Kim, Caldairou, Bernasconi, & Bernasconi, 2018). With regard to hippocampus, our pipeline performs comparable to state-of-the-art methods (Collins & Pruessner, 2010; Coupé et al, 2011; Leung et al, 2010; Platero & Tobar, 2016; Wang et al, 2013).…”