Dual geometry-inspired structural modeling for neuroendocrine tumor segmentation in endoscopic ultrasound

The increasing incidence of lower gastrointestinal neuroendocrine tumors (NETs) necessitates improved methods for early and accurate detection. Automatic segmentation of NETs in endoscopic ultrasound (EUS) images is particularly challenging due to low image contrast and indistinct tumor boundaries. This study proposes and validates GismEUS, a geometry-aware deep learning model for automated NET segmentation in EUS images.

We propose GismEUS, a deep learning architecture that integrates dual features. The model employs Endoscopic Planar Geometric Feature Projection to capture fine-grained local features and Endoscopic Stereoscopic Structural Feature Modeling to extract comprehensive global features. These feature sets are fused by the Dual Structure-Guided Feature Enhancement module, which applies both semantic-aware and distance-aware attention and subsequently combines their outputs via a local-global gated fusion. The model was trained and evaluated on a private annotated dataset for EUS images of NETs and the public GIST514-DB dataset.

GismEUS demonstrated superior segmentation performance across multiple evaluation metrics, achieving a Dice score of 0.6735, and significantly outperformed established benchmarks on the EUS datasets. These comprehensive results validate the effectiveness of the proposed feature integration strategy in addressing the unique challenges of EUS imaging.

This study demonstrates that the novel combination of planar and stereoscopic features significantly improves segmentation accuracy and introduces a high-quality annotated dataset for NETs segmentation in EUS images. GismEUS model offers a reliable and accurate automated tool for early NETs diagnosis, supporting more consistent clinical decision-making and potentially improving patient outcomes.