xIV-LDDMM Toolkit: A Suite of Image-Varifold Based Technologies for Representing and Mapping 3D Imaging and Spatial-omics Data Simultaneously Across Scales.

Autor:	Stouffer KM; Center for Imaging Science, Johns Hopkins University, Baltimore,MD, USA.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore,MD, USA.; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.; Centre Borelli ENS Paris-Saclay, Gif-Sur-Yvette, France., Chen X; Allen Institute for Brain Science, Seattle,WA, USA., Zeng H; Allen Institute for Brain Science, Seattle,WA, USA., Charlier B; IMAG, Université de Montpellier, CNRS, Montpellier, France., Younes L; Center for Imaging Science, Johns Hopkins University, Baltimore,MD, USA.; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA., Trouvé A; Centre Borelli ENS Paris-Saclay, Gif-Sur-Yvette, France., Miller MI; Center for Imaging Science, Johns Hopkins University, Baltimore,MD, USA.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore,MD, USA.; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Nov 05. Date of Electronic Publication: 2024 Nov 05.
DOI:	10.1101/2024.11.04.621983
Abstrakt:	Advancements in imaging and molecular techniques enable the collection of subcellular-scale data. Diversity in measured features, resolution, and physical scope of capture across technologies and experimental protocols pose numerous challenges to integrating data with reference coordinate systems and across scales. This resource paper describes a collection of technologies that we have developed for cross-modality 3D mapping for the alignment of transcriptomics at the micron scales of genes and cells to the anatomical tissue scales. Our collection of technologies include (i) an explicit censored data representation for the partial matching problem mapping whole brains to subsampled subvolumes, (ii) image-varifold measure norms for supporting nearly universal crossing of modality, (iii) a multi, scale-space optimization technology for generating resampling grids optimized to represent spatial geometry at fixed complexities, and (iv) mutual-information based functional feature selection. Collectively, these methods afford efficient representations of peta-scale imagery providing the algorithms for mapping from the nano to millimeter scales which we term cross-modality image-varifold LDDMM (xIV-LDDMM). Competing Interests: Competing Interests. Under a license agreement between AnatomyWorks and the Johns Hopkins University, Dr. Miller and the University are entitled to royalty distributions related to technology described in the study discussed in this. Dr. Miller is a founder of and holds equity in AnatomyWorks. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. The remaining authors declare no conflicts of interest.
Databáze:	MEDLINE
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