InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping
Saurabh Joshi, André Forjaz, Kyu Sang Han, Yu Shen, Vasco Queiroga, Florin A. Selaru, Marie Gérard, Daniel Xenes, Jordan Matelsky, Brock A. Wester, Arrate Muñoz‐Barrutia, Ashley Kiemen, Pei-Hsun Wu, Denis Wirtz
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. InterpolAI leverages optimal flow-based artificial intelligence to produce synthetic images between pairs of images for diverse three-dimensional image types. InterpolAI is more robust and accurate than existing methods, improving data quality for downstream analysis.