Endoscopic Adaptive Transformer for Enhanced Polyp Segmentation in Endoscopic Imaging.

Abstract

Polyp segmentation in endoscopic imaging is essential for the early detection of colorectal cancer, as polyps are precursor lesions in the colon and rectum, yet the task is complicated by the morphological variability and indistinct boundaries of polyps, which often blend into surrounding tissues. Conventional approaches struggle with these complexities, as fixed scale and window sizes are unable to adapt to the diverse and irregular structures of polyps. To address this challenge, we introduce the Endoscopic Adaptive Transformer, EAT, a novel framework specifically engineered for polyp segmentation. EAT incorporates an adaptive perception module, APM, that employs an adaptive perceptive-field mechanism to dynamically capture both fine-grained local details and broad contextual information, enhancing segmentation accuracy across diverse polyp morphologies. EAT demonstrates comprehensive performance by achieving a Dice coefficient of 97.77% and an HD95 of 4.50mm in single-target segmentation, while also excelling in multi-target scenarios with a Dice coefficient of 88.02% and an HD95 of 53.75mm, significantly outperforming state-of-the-art methods across both single- and multi-target segmentation scenarios. This performance underscores EAT's critical role in improving the accuracy of polyp segmentation, highlighting its potential to advance diagnostic precision and treatment planning in clinical endoscopy applications. Code: https://github.com/deepang-ai/EAT.