# Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2

## TL;DR

This paper shows that the Segment Anything Model 2 can reduce the manual effort needed to annotate CT and MRI scans while maintaining high-quality segmentation results.

## Contribution

The study introduces a workflow using SAM 2 to reduce annotation workload in medical imaging with minimal loss in segmentation accuracy.

## Key findings

- 30% of SAM 2-generated masks for femoral bone segmentation in MRI were accepted without modification.
- 53% of SAM 2-generated masks for liver tumor segmentation in CT were accepted without modification.
- Segmentation models trained on SAM 2-assisted masks achieved nearly identical performance to those trained on ground truth masks.

## Abstract

Volumetric segmentation in CT and MRI is valuable for artificial intelligence workflows in radiology, yet creating the large, precisely annotated datasets required for training segmentation models remains laborious.

Here, we tested in simulation whether the foundation model “Segment Anything Model 2” (SAM 2) can reduce expert annotation workload. In our workflow, annotators provide a single box at the object’s center, and SAM 2 automatically segments the object across slices; annotators then review and correct the masks as needed. Workload reduction was defined as the proportion of SAM 2’s predicted segmentation masks that were accepted without modification. Downstream segmentation models were then trained on the SAM 2-assisted masks and compared with reference models trained on ground truth masks.

For femoral bone segmentation in MRI and liver tumor segmentation in CT, 36,614 sagittal and 16,311 axial slices were annotated, with 30% and 53% of SAM 2-generated masks accepted as is, respectively, indicating workload reduction. Crucially, segmentation models trained on SAM 2-assisted masks performed comparably to reference models, with a median dice similarity coefficient of 98.5% compared with 98.7% for femoral bone segmentation, and 77.3% compared with 77.0% for liver tumor segmentation.

Using SAM 2 could thus expedite 3D medical imaging dataset annotation and segmentation model development for both research and clinical applications.