InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping.

Saved in:
Bibliographic Details
Title: InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping.
Authors: Joshi S; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA., Forjaz A; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA., Han KS; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA., Shen Y; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA., Queiroga V; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA., Selaru FA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA., Gérard M; Scenario, Covina, CA, USA., Xenes D; Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA., Matelsky J; Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA., Wester B; Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA., Barrutia AM; Bioengineering Department, Universidad Carlos III de Madrid and Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain., Kiemen AL; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.; Department of Functional Anatomy and Evolution, Johns Hopkins School of Medicine, Baltimore, MD, USA., Wu PH; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA., Wirtz D; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. wirtz@jhu.edu.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA. wirtz@jhu.edu.; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA. wirtz@jhu.edu.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA. wirtz@jhu.edu.
Source: Nature methods [Nat Methods] 2025 Jul; Vol. 22 (7), pp. 1556-1567. Date of Electronic Publication: 2025 May 28.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101215604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1548-7105 (Electronic) Linking ISSN: 15487091 NLM ISO Abbreviation: Nat Methods Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1548-7105
DOI:10.1038/s41592-025-02712-4