Deep learning presents novel opportunities for the auto-segmentation of gross
tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods
usually necessitate significant manual refinement. This study investigates the
Segment Anything Model (SAM), recognized for requiring minimal human prompting
and its zero-shot generalization ability across natural images. We specifically
examine MedSAM, a version of SAM fine-tuned with large-scale public medical
images. Despite its progress, the integration of multi-modality images (CT,
PET, MRI) for effective GTV delineation remains a challenge. Focusing on SAM's
application in HNC GTV segmentation, we assess its performance in both
zero-shot and fine-tuned scenarios using single (CT-only) and fused
multi-modality images. Our study demonstrates that fine-tuning SAM
significantly enhances its segmentation accuracy, building upon the already
effective zero-shot results achieved with bounding box prompts. These findings
open a promising avenue for semi-automatic HNC GTV segmentation.

本研究通过使用深度学习方法自动分割头颈部癌症的肿瘤体积，并在使用不同模态图像进行 fine-tuning 的情况下对模型性能进行评估。结果表明，在使用边界框提示的情况下，对 SAM 进行 fine-tuning 可以显著提高其分割准确度，为半自动头颈部癌症肿瘤体积分割开辟了一个有前景的途径。