Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP

Abstract

In the context of inter subject brain surface matching, we present a parameterization of the cortical surface constrained by a model of cortical organization. The parameterization is defined via an harmonic mapping of each hemisphere surface to a rectangular planar domain that integrates a representation of the model. As opposed to previous landmark-based registration methods we do not match folds between individuals but instead optimize the fit between cortical sulci and specific iso-coordinate axis in the model. This strategy overcomes some limitation to sulcus-based registration techniques such as topological variability in sulcal landmarks across subjects. Experiments on 62 subjects with manually traced sulci are presented and compared with the result of the Freesurfer software. The evaluation involves a measure of dispersion of sulci with both angular and area distortions. We show that the model-based strategy can lead to a natural, efficient and very fast (less than 5 min per hemisphere) method for defining inter subjects correspondences. We discuss how this approach also reduces the problems inherent to anatomically defined landmarks and open the way to the investigation of cortical organization through the notion of orientation and alignment of structures across the cortex.

Brain Lobes Revealed by Spectral Clustering

“…Cluster matching 1) Surface parameterization with "HIP-HOP": We present here a parameterization method that will allow to evaluate the reproducibility of cortical segmentations in K surface patches across a group of subjects. We use a recent model-driven harmonic parameterization of the cortical surface called "HIP-HOP" [15]. This approach allows to find a bijective mapping h between any surface point not in the insula I nor in the corpus callosum C and two coordinates in a rectangle of prescribed dimensions: h : S \ {C ∪ I} −→ [0, L] × [0, w] with two soft constraints:…”

“…We have tested our approach on the group of n = 62 healthy subjects previously used in [15]. At the moment we have only examined the left hemispheres of their brains segmented from T1-weighted MRI with FreeSurfer software 1 .…”

“…For this we propose first an individual segmentation strategy based on spectral clustering. Next the HIP-HOP parameterization [15] allows to project every segmentation maps on a same spherical domain. Last we propose a simple way to match the different clusters in order to build a consensus segmentation.…”

Primatologist: A modular segmentation pipeline for macaque brain morphometry

“…neuroimage.2017.09.00 * Corresponding author: Thierry Delzescaux (thierry.delzescaux@cea.fr) the brain topography either through the registration of volumes (Ashburner, 2000) or surfaces (Fischl et al, 1999) towards a template, or through a theoretical coordinate system (Auzias et al, 2013;Régis et al, 2005;Talairach and Tournoux, 1988).…”

Model-driven parameterization of fetal cortical surfaces

“…As a whole, data-driven surface registration techniques are not appropriate due to the lack of consistent structural features such as the primary sulci across all GAs, especially for young fetuses. In this work, we present an adaptation of the HIP-HOP model-driven cortical surface parameterization method [7] to the specific problem of defining correspondences across fetuses with different GA. Using structural information from in utero MRI and defining a common model across GAs, we perform morphometric analysis of early folding during cortical plate surface development in the normal fetal brain between 21 to 34 weeks of gestation.…”