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Background: Tumor segmentation in MRI is crucial in radiotherapy (RT)treatment planning for brain tumor patients. Segment anything (SA), a novelpromptable foundation model for autosegmentation, has shown high accuracy formultiple segmentation tasks but was not evaluated on medical datasets yet.Methods: SA was evaluated in a point-to-mask task for glioma brain tumorautosegmentation on 16744 transversal slices from 369 MRI datasets (BraTS2020). Up to 9 point prompts were placed per slice. Tumor core (enhancing tumor+ necrotic core) was segmented on contrast-enhanced T1w sequences. Out of the 3masks predicted by SA, accuracy was evaluated for the mask with the highestcalculated IoU (oracle mask) and with highest model predicted IoU (suggestedmask). In addition to assessing SA on whole MRI slices, SA was also evaluatedon images cropped to the tumor (max. 3D extent + 2 cm). Results: Mean best IoU(mbIoU) using oracle mask on full MRI slices was 0.762 (IQR 0.713-0.917). Best2D mask was achieved after a mean of 6.6 point prompts (IQR 5-9). Segmentationaccuracy was significantly better for high- compared to low-grade glioma cases(mbIoU 0.789 vs. 0.668). Accuracy was worse using MRI slices cropped to thetumor (mbIoU 0.759) and was much worse using suggested mask (full slices0.572). For all experiments, accuracy was low on peripheral slices with fewtumor voxels (mbIoU, <300: 0.537 vs. >=300: 0.841). Stacking best oraclesegmentations from full axial MRI slices, mean 3D DSC for tumor core was 0.872,which was improved to 0.919 by combining axial, sagittal and coronal masks.Conclusions: The Segment Anything foundation model, while trained on photos,can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRIslices. The results suggest that Segment Anything can accelerate and facilitateRT treatment planning, when properly integrated in a clinical application.

The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning