Using nnU-Net V2 with a 15-fold ensemble, the authors report aggregated Dice scores of 0.81 (pre-RT) and 0.70 (mid-RT) for head and neck tumor segmentation on MRI.
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images
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abstract
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.
fields
physics.med-ph 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble
Using nnU-Net V2 with a 15-fold ensemble, the authors report aggregated Dice scores of 0.81 (pre-RT) and 0.70 (mid-RT) for head and neck tumor segmentation on MRI.