ArticleNature methods2025
InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
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- Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.Nature biomedical engineering · 2026Article
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- Fault-tolerant 3D reconstruction from 2D spatial proteomics sections.bioRxiv : the preprint server for biology · 2026Article
- Article
- Whole organism 3D mapping reveals universal branching topology and biophysical optimization governs vascular and nervous system development.bioRxiv : the preprint server for biology · 2026Article
- UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction.bioRxiv : the preprint server for biology · 2026Article
- Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer.bioRxiv : the preprint server for biology · 2026Article
- Advanced imaging strategies in cardiac organoids: bridging the gap between structural complexity and functional analysis.Cardiovascular diabetology · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Automation of Detector Array Design for Baggage X-Ray Scanners.Sensors (Basel, Switzerland) · 2025Article
- Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues.Cell reports methods · 2025Article
- AI-powered 3D pathology protocol enhances enteric nervous system visualization and quantification for clinical diagnostics.Theranostics · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
Recent advances in imaging and computation have enabled analysis of large three-dimensional (3D) biological datasets, revealing spatial composition, morphology, cellular interactions and rare events. However, the accuracy of these analyses is limited by image quality, which can be compromised by missing data, tissue damage or low resolution due to mechanical, temporal or financial constraints. Here, we introduce InterpolAI, a method for interpolation of synthetic images between pairs of authentic images in a stack of images, by leveraging frame interpolation for large image motion, an optical flow-based artificial intelligence (AI) model. InterpolAI outperforms both linear interpolation and state-of-the-art optical flow-based method XVFI, preserving microanatomical features and cell counts, and image contrast, variance and luminance. InterpolAI repairs tissue damages and reduces stitching artifacts. We validated InterpolAI across multiple imaging modalities, species, staining techniques and pixel resolutions. This work demonstrates the potential of AI in improving the resolution, throughput and quality of image datasets to enable improved 3D imaging.
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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 Socratic graph.