Improving medical image segmentation with SAM2: analyzing the impact of object characteristics and finetuning on multi-planar datasets.

Objective This study investigates the factors that affect the performance of the Segment Anything Model 2 (SAM2) on medical imaging datasets, with a specific focus on the influence of object characteristics and the benefits of fine-tuning on multi-planar datasets. Methods Utilizing data from three comprehensive medical imaging datasets—Medical Segmentation Decathlon (MSD), ISLES 2022, and BTCV Multi-Organ Abdominal Dataset—we analyzed SAM2's segmentation accuracy across a variety of object characteristics, such as size, intensity, location, and structural complexity. Our dataset included 714 cases, representing a various anatomical region and an independent test set of 985 video examples was used to validate our findings. Results Fine-tuning SAM2 led to notable improvements in segmentation performance across all metrics. The global mean Intersection over Union (IoU) increased from 0.690 to 0.827 while the Dice coefficient and Normalized Surface Dice (NSD) saw improvements of 15.58 % and 14.6 %, respectively. Challenging structures showed the most dramatic improvements, with the pancreas displaying a remarkable 48.8 % increase in Dice score and a 65.2 % improvement in IoU post-finetuning. Statistical analyses demonstrated significant correlations between segmentation performance and object characteristics. Medium-sized, centrally located structures with high solidity and smooth boundaries achieved the highest performance metrics. Conclusion SAM2's segmentation performance is affected by object characteristics like size, location, and structural complexity. Fine-tuning the model with medical imaging data markedly enhances its accuracy, underlining SAM2's potential as a robust tool for clinical and research applications. The software, data, and resulting model are publicly accessible for non-commercial use. Our code will be released at: https://github.com/RadSam2/rad_sam2