ArticlePLoS computational biology2018
On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 22 citations in OpenAlex.
- Scalable spatial transcriptomics through computational array reconstruction.Nature biotechnology · 2026Article
- Dendritome mapping reveals the spatial organization of striatal neuron morphology.Nature neuroscience · 2025Article
- Solving the where problem and quantifying geometric variation in neuroanatomy using generative diffeomorphic mapping.Nature communications · 2025Article
- The xIV-LDDMM toolkit of image-varifold based technologies for mapping 3D images and spatial-omics across scales.Communications biology · 2025Article
- Multidimensional Alternating Kernel Method for cortical layer segmentation in 3D reconstructed histology.MethodsX · 2024Article
- A Majorization-Minimization Algorithm for Neuroimage Registration.SIAM journal on imaging sciences · 2024Article
- A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Projective Diffeomorphic Mapping of Molecular Digital Pathology with Tissue MRI.Communications engineering · 2022Article
- Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings.The Journal of comparative neurology · 2021Article
- Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease.Frontiers in neuroscience · 2020Article
- Relation of koniocellular layers of dorsal lateral geniculate to inferior pulvinar nuclei in common marmosets.The European journal of neuroscience · 2019Article
- Traumatic microbleeds suggest vascular injury and predict disability in traumatic brain injury.Brain : a journal of neurology · 2019Article
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4 authors at 2 institutions in 1 country.
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Abstract
This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.