ArticleMedical image analysis2022
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A unified 3D reconstruction of microscopy and MRI in a brain showing Alzheimer's disease-related neuropathology.Brain pathology (Zurich, Switzerland) · 2026Article
- [An unsupervised three-dimensional medical image registration method based on shifted window Transformer and convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling.Acta neuropathologica communications · 2025Article
- Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Ultrahigh-resolution 7-Tesla anatomic magnetic resonance imaging and diffusion tensor imaging ofFrontiers in human neuroscience · 2024Article
- Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer.Med (New York, N.Y.) · 2023Review
- Article
- High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning.NeuroImage · 2022Review
- Bridging Neuroimaging and Neuropathology: A Comprehensive Workflow for Targeted Sampling of White Matter Lesions.Journal of neuroimaging : official journal of the American Society of NeuroimagingReview
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9 authors.
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Abstract
Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ
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