Joshi Saurabh, Forjaz André, Han Kyu Sang, Shen Yu, Queiroga Vasco, Selaru Florin A, Gérard Marie, Xenes Daniel, Matelsky Jordan, Wester Brock, Barrutia Arrate Muñoz, Kiemen Ashley L, Wu Pei-Hsun, Wirtz Denis
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
Nat Methods. 2025 May 28. doi: 10.1038/s41592-025-02712-4.
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.
成像和计算领域的最新进展使得对大型三维（3D）生物数据集进行分析成为可能，揭示了空间组成、形态、细胞相互作用和罕见事件。然而，这些分析的准确性受到图像质量的限制，由于机械、时间或经济限制导致的数据缺失、组织损伤或低分辨率，图像质量可能会受到影响。在此，我们介绍了InterpolAI，这是一种通过利用用于大图像运动的帧插值（一种基于光流的人工智能（AI）模型），在图像堆栈中的成对真实图像之间插值合成图像的方法。InterpolAI在保留微观解剖特征和细胞计数以及图像对比度、方差和亮度方面优于线性插值和基于光流的最先进方法XVFI。InterpolAI修复组织损伤并减少拼接伪影。我们在多种成像模式、物种、染色技术和像素分辨率上对InterpolAI进行了验证。这项工作证明了人工智能在提高图像数据集的分辨率、通量和质量以实现改进的3D成像方面的潜力。
