Segment anything model for head and neck tumor segmentation with CT, PET
  and MRI multi-modality images
Jintao Ren Mathis Rasmussen Jasper Nijkamp Jesper Grau Eriksen Stine Korreman
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.