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Open-source pre-clinical image segmentation: mouse cardiac magnetic resonance imaging datasets with a deep learning
Wan Shah1, Daniel J Stuckey2, Tina Yao3
1UCL Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, London, UK; UCL Centre for Advanced Biomedical Imaging, University College London, London, UK.
Summary
We developed an open-source deep learning model for mouse cardiac MRI segmentation, achieving high accuracy and speed. This accelerates pre-clinical cardiovascular research by providing a reproducible benchmark.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Pre-clinical Research
Background:
- Manual segmentation of cardiac MRI in mice is time-consuming and variable.
- Existing deep learning models do not generalize to pre-clinical cardiac MRI data.
- Lack of public datasets and models hinders large-scale pre-clinical studies.
Purpose of the Study:
- To present the first publicly-available pre-clinical cardiac MRI dataset.
- To develop and release an open-source deep learning segmentation model for mouse cardiac MRI.
- To establish a benchmark for pre-clinical cardiac MRI analysis.
Main Methods:
- Created a dataset of cine short-axis cardiac MRI from 130 mice with diverse phenotypes at 9.4T.
- Included expert manual segmentations for left ventricular blood pool and myocardium.
- Developed an open-source UNet3+ based deep learning model for segmentation.
- Evaluated the model on internal and external test datasets across different field strengths (7T, 9.4T, 11.7T).
Main Results:
- Deep learning model achieved segmentation in ~4.6s per cine stack, over 6,000x faster than manual analysis.
- High segmentation accuracy with Dice scores ≥ 0.91 for blood pool and myocardium.
- Excellent agreement for functional parameters (ICC ≥ 0.89), comparable to human inter-observer variability.
Conclusions:
- The first open-access mouse cardiac MRI dataset and open-source DL model are provided.
- This resource establishes a benchmark for pre-clinical cardiac MRI.
- Enables reproducible, scalable, and community-driven development for cardiovascular research acceleration.
