  In this study, we evaluate the performance of the Segment Anything Model
(SAM) in clinical radiotherapy. Our results indicate that SAM's 'segment
anything' mode can achieve clinically acceptable segmentation results in most
organs-at-risk (OARs) with Dice scores higher than 0.7. SAM's 'box prompt' mode
further improves the Dice scores by 0.1 to 0.5. Considering the size of the
organ and the clarity of its boundary, SAM displays better performance for
large organs with clear boundaries but performs worse for smaller organs with
unclear boundaries. Given that SAM, a model pre-trained purely on natural
images, can handle the delineation of OARs from medical images with clinically
acceptable accuracy, these results highlight SAM's robust generalization
capabilities with consistent accuracy in automatic segmentation for
radiotherapy. In other words, SAM can achieve delineation of different OARs at
different sites using a generic automatic segmentation model. SAM's
generalization capabilities across different disease sites suggest that it is
technically feasible to develop a generic model for automatic segmentation in
radiotherapy.
