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

Frontiers in Neuroinformatics

卷:16

Antoine Balzeau¹  William Hopkins²  Olivier Coulon³  Kep Kee Loh³  Martial Mancip⁴  Nicole Labra⁵  Bastien Cagna⁶  Jean-François Mangin⁶  Denis Rivière⁶  Yann Cointepas⁶  Nabil Vindas⁶  Ophélie Foubet⁶  Yann Leprince⁶  Jessica Lebenberg⁷ 

[1] Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium;

[2] Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States;

[3] INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France;

[4] Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France;

[5] PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France;

[6] Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France;

[7] Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France;

关键词: visualization;    brain atlas;    inter-subject;    3D;    parcellation atlas;    structural approach;   

DOI  :  10.3389/fninf.2022.803934

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown