ArticleFrontiers in neuroscience2020
Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images.bioRxiv : the preprint server for biology · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 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
- Skeletonization of neuronal processes using Discrete Morse techniques from computational topology.Research square · 2025Article
- Skeletonization of neuronal processes using Discrete Morse techniques from computational topology.bioRxiv : the preprint server for biology · 2025Article
- Skeletonization of neuronal processes using Discrete Morse techniques from computational topology.ArXiv · 2025Article
- RegBoost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation.NeuroImage · 2025Article
- Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections.Nature communications · 2024Article
- A Majorization-Minimization Algorithm for Neuroimage Registration.SIAM journal on imaging sciences · 2024Article
- Article
- Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data.Neuroinformatics · 2023Article
- Applicability and usage of dose mapping/accumulation in radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Review
- The cell type composition of the adult mouse brain revealed by single cell and spatial genomics.bioRxiv : the preprint server for biology · 2023Article
- Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer.Med (New York, N.Y.) · 2023Review
- Regression models for partially localized fMRI connectivity analyses.Frontiers in neuroimaging · 2023Article
- Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease.NeuroImage. Clinical · 2023Article
- Article
- Molecular Computational Anatomy: Unifying the Particle to Tissue Continuum via Measure Representations of the Brain.BME frontiers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.