From hours to seconds: a new tool for accelerating in vivo rodent imaging data analysis using AI algorithms

Background: Advances in noninvasive imaging for cancer research have increased the demand for larger animal cohorts to achieve statistical power. However, in vivo image analysis remains challenging, often requiring skilled users to manually process organs or tumors in 2D and 3D images-a time-intensive task that can take hours to weeks depending on study size. The need to master multiple software platforms for different imaging modalities further extends analysis time, often surpassing image acquisition time. Methods: To address these challenges, we developed a Python-based multimodal software application designed to accelerate data processing through AI-assisted segmentation and batch analysis across multiple timepoints. Here, we report the performance of the software and multiple integrated AI segmentation models for ultrasound and optical imaging. Results: Analysis throughput improved by 9x for 3D ultrasound and up to 60x for 2D optical imaging compared to manual workflows. The software demonstrated strong agreement compared to human ground truth segmentations and ex vivo validation standards. BLI Imaging: Deep learning-based masking showed near-perfect agreement with standard quantification methods (R^2 = 0.995 vs circular ROI; R^2 = 0.996 vs bounding boxes) while reducing analysis time from 15-20 seconds to ~2 seconds per study. Ultrasound Imaging: AI-measured spleen size correlated strongly with postmortem spleen weights (R^2 = 0.93) and MRI volumes (R^2 = 0.90). AI segmentations achieved an average Dice score of 0.89 against ground truth human segmentations with predicted volumes correlating at R^2 = 0.95. For subcutaneous tumors, agreement was lower, but still strong when comparing AI versus human segmentations (Dice = 0.82; R^2 = 0.78) despite a more challenging heterogeneous echotexture profile. Conclusions: AI-driven automation significantly accelerates multimodal image analysis without compromising accuracy. These advances highlight the potential of integrated automation to streamline preclinical imaging workflows and enhance research efficiency.