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

Journal: Nature methods
Published Date: May 28, 2025

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.

Authors

  • Saurabh Joshi
    Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India.
  • André Forjaz
    Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Kyu Sang Han
    Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Yu Shen
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.
  • Vasco Queiroga
    Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Florin A Selaru
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Marie Gérard
    Scenario, Covina, CA, USA.
  • Daniel Xenes
    Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
  • Jordan Matelsky
    Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America.
  • Brock Wester
    The Johns Hopkins University Applied Physics Laboratory, Research and Exploratory Development Department, Laurel, MD, 20723, USA.
  • Arrate Muñoz Barrutia
    Bioengineering Department, Universidad Carlos III de Madrid and Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
  • Ashley L Kiemen
  • Pei-Hsun Wu
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Denis Wirtz
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA; Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA; Department of Oncology, Johns Hopkins School of Medicine, 1800 Orleans St, Baltimore, MD, 21205, USA. Electronic address: wirtz@jhu.edu.

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