Optimizing Artificial Intelligence - Segmentation of Sinonasal Masses in Endoscopic Images Through Augmentation of Meta's Segment Anything Model

Objective: Accurate segmentation of sinonasal lesions is essential for developing AI-based applications in endoscopic procedures. Current segmentation models are developed primarily using convolutional neural network (CNN) architecture, and plateau at an accuracy of ~0.75. Our objective is to develop an augmented Meta’s Segment Anything Model (SAM) model optimized for segmentation of sinonasal masses in endoscopy images. Methods: We integrated SAM with a ResNet-50 CNN using cross-attention layers and trained the model on 1,242 endoscopy images from patients evaluated at an otolaryngology center ([ Fig. 1 ]). Images included tumors, polyps, or normal findings. Images were annotated and independently validated by 3 otolaryngologists. We compared performance of augmented SAM to nnUNet and zero-shot SAM using Dice similarity coefficient and Intersection-over-Union (IoU) metrics, applying an 8:1:1 train-validation-test split. For external validation, we tested augmented SAM on a publicly available dataset from another institution. Results: Augmented SAM achieved a Dice of 0.90 and an IoU of 0.82 for polyp segmentation ([ Fig. 2 ]), outperforming nnUNet (Dice 0.74, IoU 0.60) and zero-shot SAM (DICE 0.58, IoU 0.43). For tumors ([ Fig. 3 ]), augmented SAM reached Dice 0.87 and IoU of 0.77, compared with nnUNet (Dice 0.73, IoU 0.58) and zero-shot SAM (Dice 0.61, IoU 0.45). On the external dataset, augmented SAM achieved a high accuracy for tumors (Dice 0.86) and polyps (Dice 0.90). Conclusion: Augmented SAM significantly improves segmentation accuracy for sinonasal masses, outperforming existing benchmarks. This lays the potential groundwork for development of real-time, lesion-aware AI tools in rhinologic procedures Publication History Article published online: 27 February 2026 © 2026. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany