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Abstract #3011

Automated Deep Learning Cine MRI Segmentation for Cardiac Function Assessment in Preclinical Models

Wan Hanisah1, Tina Yao2, Daniel Stuckey1, Jennifer Steeden2, and Vivek Muthurangu2
1Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom, 2Institute of Cardiovascular Science, University College London, London, United Kingdom

Synopsis

Keywords: Preclinical Image Analysis, Preclinical

Motivation: Deep learning offers potential for automated cardiac segmentation in preclinical models but remains challenging due to limited rodent CMR data and high imaging variability.

Goal(s): To develop an accurate, fully automated LV segmentation deep learning model to reduce analysis time and manual workload in preclinical cardiac assessments.

Approach: Cine SAX CMR data from 73 mice were segmented at systolic and diastolic phases, producing 910 annotated images. A 2D U-Net model optimised for small, imbalanced datasets was trained and validated using DICE similarity and cardiac volume metrics.

Results: The model achieved high accuracy and speed, enabling reliable LV assessments and supporting larger preclinical studies.

Impact: Automating left ventricular segmentation with UNet3Plus has the potential to reduce analysis time and human error in large-scale, multi-timepoint preclinical studies, improving efficiency and enabling deeper investigation into cardiac dysfunction to accelerate the development and evaluation of novel cardiovascular therapies.

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