Open-source pre-clinical image segmentation: mouse cardiac magnetic resonance imaging datasets with a deep learning segmentation framework

BACKGROUND: Longitudinal cardiovascular magnetic resonance (CMR) in small animal models is essential for studying cardiovascular disease mechanisms and assessing new therapies. However, conventional manual segmentation of ventricular structures from cine short-axis images is slow, labor-intensive, and subject to substantial observer variability, limiting reproducibility in large-scale pre-clinical studies. While deep learning (DL) segmentation has become standard in human CMR, existing models do not generalize to pre-clinical datasets. Current mouse-specific approaches have been constrained by small private cohorts, where the DL segmentation models are not published. METHODS: Here, we present the first publicly available pre-clinical CMR dataset, along with an open-source DL segmentation model and a web-based interface for easy deployment. The dataset comprises complete cine short-axis CMR images from 130 mice with diverse phenotypes, acquired at 9.4T. The dataset also contains expert manual segmentations of left ventricular (LV) blood pool and myocardium at end-diastole, end-systole, as well as additional timeframes with artifacts to improve robustness. Using this resource, we developed an open-source DL segmentation model based on the UNet3+ architecture. The model was evaluated against an independent internal test dataset (9.4T, n = 25 mice), as well as two external test datasets (n = 15 mice at 7T, and n = 10 mice at 11.7T) and compared to expert manual segmentations in terms of Dice and functional parameters. RESULTS: At inference, the DL segmentation model took 0.85 for the external test datasets), with ICC values comparable to human inter-observer variability. CONCLUSION: This work provides the first open-access mouse cardiac MRI dataset, an open-source DL segmentation model, and an accessible inference platform. These tools establish a benchmark for pre-clinical CMR, enabling reproducible, scalable, and community-driven development of DL methods to accelerate cardiovascular research.