Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound.
Authors
Affiliations (12)
Affiliations (12)
- Department of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, USA.
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ, USA.
- Department of Cardiovascular Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Center of Artificial Intelligence in Medical Imaging, Stanford University, Stanford, CA, USA.
- Department of Radiology, Stanford University, Stanford, CA, USA.
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA. [email protected].
- Department of Computer Science, Stanford University, Stanford, CA, USA. [email protected].
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA. [email protected].
- Department of Computer Science, Stanford University, Stanford, CA, USA. [email protected].
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. [email protected].
Abstract
The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.