Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Abstract

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this “iconic” approach has limits. We present in this study an alternative, complementary, “structural” approach, which consists in extracting structures from the individual data, and comparing them without deformation. A “structural atlas” is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

Segmentation of incident lacunes during the course of ischemic cerebral small vessel diseases

“…The evaluation was performed with simultaneous visualization of the baseline and follow-up images already registered in the MNI space. After an inspection of all 3D-T1 MRI slices, each incident lacune was then marked, on a single MRI slice, on the screen by each rater using the Anatomist software ( 23 ) and blinded to the patient's status. Experts did not use any specific tool to measure the exact size of the lacune.…”

Exploring the emergence of morphological asymmetries around the brain’s Sylvian fissure: a longitudinal study of shape variability in preterm infants

“…Once extracted, the sulci were automatically labeled using a Bayesian pattern recognition strategy (Perrot et al, 2011). Finally, the labelling was manually checked and corrected when necessary by one of the authors (HV) using open access software Anatomist (Rivière et al, 2022). In particular and as detailed below, we focused on the labelling of the 1) Sylvian fissure and its branches, 2) sulci of the perisylvian region, and 3) sulci of the infero-posterior region of the frontal lobe.…”

Sulcation in children with autism spectrum disorders

“…This de nition offers a reliable and robust representation of the sulcal surface that is unaffected by changes in cortical thickness or gray matter/white matter contrast [33] brought on by, for instance, developmental processes or medical treatments. Images were visually inspected using the viewer Anatomist of BrainVISA [34]. There were no obvious segmentation errors (such as cortical ribbon thinned, gyrus or sulcus absent) found.…”