Chang, Christian Law, Hudson Poon, Connor Yen, Sydney Lall, Kaustubh Jamshidi, Armin Malis, Vadim Hwang, Dosik Bae, Won C. 2025-07-18T06:31:03Z 2025-07-18T06:31:03Z 2025-07-18 2025-06 https://pubs.kist.re.kr/handle/201004/152784 Lumbar spine Magnetic Resonance Imaging (MRI) is commonly used for intervertebral disc (IVD) and vertebral body (VB) evaluation during low back pain. Segmentation of these tissues can provide useful quantitative information such as shape and volume. The objective of the study was to determine the performances of Segment Anything Model (SAM) and medical SAM (MedSAM), two "zero-shot" deep learning models, in segmenting lumbar IVD and VB from MRI images and compare against the nnU-Net model. This cadaveric study used 82 donor spines. Manual segmentation was performed to serve as ground truth. Two readers processed the spine MRI using SAM and MedSAM by placing points or drawing bounding boxes around regions of interest (ROI). The outputs were compared against ground truths to determine Dice score, sensitivity, and specificity. Qualitatively, results varied but overall, MedSAM produced more consistent results than SAM, but neither matched the performance of nnU-Net. Mean Dice scores for MedSAM were 0.79 for IVDs and 0.88 for VBs, and significantly higher (each p < 0.001) than those for SAM (0.64 for IVDs, 0.83 for VBs). Both were lower compared to nnU-Net (0.99 for IVD and VB). Sensitivity values also favored MedSAM. These results demonstrated the feasibility of "zero-shot" DL models to segment lumbar spine MRI. While performance falls short of recent models, these zero-shot models offer key advantages in not needing training data and faster adaptation to other anatomies and tasks. Validation of a generalizable segmentation model for lumbar spine MRI can lead to more precise diagnostics, follow-up, and enhanced back pain research, with potential cost savings from automated analyses while supporting the broader use of AI and machine learning in healthcare. English Multidisciplinary Digital Publishing Institute (MDPI) Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI Article 10.3390/s25123596 1 Sensors, v.25, no.12 Sensors 25 12 Y scie scopus 001516731600001 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article DEGENERATION IMAGE intervertebral disc vertebral body artificial intelligence deep learning vision transformer image segmentation promptable zero shot