Abdominal Foundation Model: Bootstrapping artificial intelligence for MRI organ volume biomarker analysis in ADPKD

Motivation: Abdominal organ volumes are critical MRI biomarkers in many diseases including autosomal dominant polycystic kidney disease. Goal(s): We aim to develop a segmentation model with an enhanced ability to generalize across various abdominal organs and MR pulse sequences. Approach: We construct a multi-modality abdominal foundation model expanding upon our existing ADPKD kidney model which adapts to diverse organs and tissues with minimal new training data. Results: The model was trained using a model-in-loop methodology and evaluated against radiologist benchmarks, yielding an impressive Dice score of 0.94 for in-distribution sequences and 0.73 for organ segmentations on out-of-distribution sequences. Impact: This foundational model can seamlessly integrate into clinical workflows, utilizing routine cases to enhance its performance and extending its application to additional organs and tissues. This advance also marks a significant step toward the automation of MRI reporting.