High Throughput In vivo Imaging; A Pipeline for Automated Analysis of Longitudinal Preclinical Studies

Fast acquisition times, multi-animal beds, and improved accessibility of preclinical imaging systems have enabled high-throughput imaging, significantly accelerating in vivo experimentation. However, image analysis remains largely manual, labor-intensive, and inefficient, with significant inter- and intra-user variability, adding to the already lengthy timeline of preclinical validation. This article outlines an end-to-end pipeline for image acquisition and subject preprocessing, followed by deep learning-based segmentation and reporting of metrics relevant for validation. This pipeline is applicable to imaging studies investigating biodistribution, dosimetry, disease progression, and treatment effect over time. A longitudinal positron emission tomography-computed tomography (PET-CT) case study is used to demonstrate the protocol in a practical animal research context. The protocol encompasses image acquisition and reconstruction, fusion, cropping, sorting, metadata addition, deep learning-based and interactive organ and tumor segmentation, 3-dimensional visualization, and extraction of quantitative uptake curves. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Image Acquisition and Reconstruction Basic Protocol 2: Multi-Subject Cropping and Sorting Basic Protocol 3: Atlas Alignment and Segmentation Basic Protocol 4: Quantitation and Reporting.